• Journal of Computational Design and Engineering
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• 제3권1호
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• pp.71-90
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• 2016

Steel nitriding is a thermo-chemical process leading to surface hardening and improvement in fatigue properties. The process is strongly influenced by many different variables such as steel composition, nitrogen potential, temperature, time, and quenching media. In the present study, the influence of such parameters affecting physic-chemical and mechanical properties of nitride steels was evaluated. The aim was to streamline the process by numerical-experimental analysis allowing defining the optimal conditions for the success of the process. Input parameters-output results correlations were calculated through the employment of a multi-objective optimization software, modeFRONTIER (Esteco). The mechanical and microstructural results belonging to the nitriding process, performed with different processing conditions for various steels, are presented. The data were employed to obtain the analytical equations describing nitriding behavior as a function of nitriding parameters and steel composition. The obtained model was validated, through control designs, and optimized by taking into account physical and processing conditions.

• Structural Engineering and Mechanics
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• 제86권5호
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• pp.647-661
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• 2023

It is recognized that the installation of energy dissipation devices, such as the tuned mass damper (TMD), decreases the dynamic response of structures, however, the best parameters of each device persist hard to determine. Unlike many works that perform only a deterministic optimization, this work proposes a complete methodology to minimize the dynamic response of footbridges by optimizing the parameters of multiple tuned mass dampers (MTMD) taking into account uncertainties present in the parameters of the structure and also of the human excitation. For application purposes, a steel footbridge, based on a real structure, is studied. Three different scenarios for the MTMD are simulated. The proposed robust optimization problem is solved via the Circle-Inspired Optimization Algorithm (CIOA), a novel and efficient metaheuristic algorithm recently developed by the authors. The objective function is to minimize the mean maximum vertical displacement of the footbridge, whereas the design variables are the stiffness and damping constants of the MTMD. The results showed the excellent capacity of the proposed methodology, reducing the mean maximum vertical displacement by more than 36% and in a computational time about 9% less than using a classical genetic algorithm. The results obtained by the proposed methodology are also compared with results obtained through traditional TMD design methods, showing again the best performance of the proposed optimization method. Finally, an analysis of the maximum vertical acceleration showed a reduction of more than 91% for the three scenarios, leading the footbridge to acceleration values below the recommended comfort limits. Hence, the proposed methodology could be employed to optimize MTMD, improving the design of footbridges.

• Structural Engineering and Mechanics
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• 제63권3호
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• pp.303-315
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• 2017

This paper presents a review on getting a Weighted Multi-Objective Optimization (WMO) of Tuned Mass Damper (TMD) parameters based on Response Surface Methodology (RSM) coupled central composite design and Weighted Desirability Function (WDF) to attenuate the earthquake vibration of a jacket supported Offshore Wind Turbine (OWT). To optimize the parameters (stiffness and damping coefficient) of damper, the frequency ratio and damping ratio were considered as a design variable and the top displacement and frequency response were considered as objective functions. The optimization has been carried out under only El Centro earthquake results and after obtained the optimal parameters, more two earthquakes (California and Northridge) has been performed to investigate the performance of optimal damper. The obtained results also compared with the different conventional TMD's designed by Den Hartog's, Sadek et al.'s and Warburton's method. From the results, it was found that the optimal TMD based on RSM shows better response than the conventional damper. It is concluded that the proposed response model offers an efficient approach regarding the TMD optimization.

• Journal of Mechanical Science and Technology
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• 제18권10호
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• pp.1722-1729
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• 2004

This paper presents a design methodology for determining configurations of slider air bearings considering the randomness of the air-bearing surface (ABS) geometry by using the iSIGHT. A reliability-based design optimization (RBDO) problem is formulated to minimize the variations in the mean values of the flying heights from a target value while satisfying the desired probabilistic constraints keeping the pitch and roll angles within a suitable range. The reliability analysis is employed to estimate how the fabrication tolerances of individual slider parameters affect the final flying attitude tolerances. The proposed approach first solves the deterministic optimization problem. Then, beginning with this solution, the RBDO is continued with the reliability constraints affected by the random variables. Reliability constraints overriding the constraints of the deterministic optimization attempt to drive the design to a reliability solution with minimum increase in the objective. The simulation results of the RBDO are listed in comparison with the values of the initial design and the results of the deterministic optimization, respectively. To show the effectiveness of the proposed approach, the reliability analyses are simply carried out by using the mean value first-order second-moment (MVFO) method. The Monte Carlo simulation of the RBDO's results is also performed to estimate the efficiency of the proposed approach. Those results are demonstrated to satisfy all the desired probabilistic constraints, where the target reliability level for constraints is defined as 0.8.

• Structural Engineering and Mechanics
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• 제47권4호
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• pp.513-530
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• 2013

In this paper, an improved version of particle swarm optimization based optimum design algorithm (IPSO) is presented for the steel grillage systems. The optimum design problem is formulated considering the provisions of American Institute of Steel Construction concerning Load and Resistance Factor Design. The optimum design algorithm selects the appropriate W-sections for the beams of the grillage system such that the design constraints are satisfied and the grillage weight is the minimum. When an improved version of the technique is extended to be implemented, the related results and convergence performance prove to be better than the simple particle swarm optimization algorithm and some other metaheuristic optimization techniques. The efficiency of different inertia weight parameters of the proposed algorithm is also numerically investigated considering a number of numerical grillage system examples.

• 한국기계가공학회지
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• 제7권2호
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• pp.8-15
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• 2008

Current trend of design technologies shows engineers to objectify or automate the given decision-making process. The numerical optimization is an example of such technologies. However, in numerical optimization, the uncertainties are uncontrollable to efficiently objectify or automate the process. To better manage these uncertainties, Taguchi method, reliability-based optimization and robust optimization are being used. Based on the independence axiom of axiomatic design theory that illustrates the relationship between desired specifications and design parameters, the designs can be classified into three types: uncoupled, decoupled and coupled. To best approach the target performance with the maximum robustness is one of the main functional requirements of a mechanical system. Most engineering designs are pertaining to either coupled or decoupled ones, but these designs cannot currently accomplish a real robustness thus a trade-off between performance and robustness has to be made. In this research, the game theory will be applied to optimize the trade-off.

• Journal of Electrical Engineering and Technology
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• 제6권2호
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• pp.182-191
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• 2011

Genetic algorithms (GA) and particle swarm optimization (PSO) are the most famous optimization techniques among various modern heuristic optimization techniques. These two approaches identify the solution to a given objective function, but they employ different strategies and computational effort; therefore, a comparison of their performance is needed. This paper presents the application and performance comparison of the PSO and GA optimization techniques for a static synchronous series compensator-based controller design. The design objective is to enhance power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem, and both PSO and GA optimization techniques are employed to search for the optimal controller parameters.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2008년 영문 학술대회
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• pp.226-234
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• 2008

Grain design has always been a vital and integral part of Solid Rocket Motor(SRM) design. Basing on the design objectives set by the system designer, the SRM designer has many options available for selecting the Grain configuration. Many of the available configurations may fulfill the required parameters of volumetric loading fraction, web fraction & Length to diameter ratios and produce internal ballistic results that may be in accordance to the design objectives. However, for any given set of design objectives, it is deemed necessary that best possible configuration be selected, designed and optimized. Hence optimal results of all applicable configurations are vital to be attained in order to compare and finalize the design that will produce most efficient performance. Generally the engineers pay attention and have skills on a specific grain configuration. The designing methodologies and computer codes available usually focus on single grain configuration may it be Star, Wagon Wheel or slotted tube. Hardly one can find a software or a design methodology where all such configurations can be worked on jointly and not only adequate designs be found but optimal solutions reached by applying an optimization method to find final design best suited for any design objective. In the present work design requirements have been set, grain configurations have been selected and their designing has been conducted. The internal ballistic parameters have been calculated and after finding the preliminary design solutions, the optimal solutions have been found. In doing so, software has been developed comprising of computer programs for designing the 2D grains including Star, Wagon Wheel and Slotted Tube configurations. The optimization toolbox of Matlab Fmincon has been used for getting optimal solutions. The affects of all the independent geometric design variables on the optimized solutions have been analyzed. Based on results attained from Optimization Method, an in depth comparison of Grain Configurations and analysis of performance prediction outputs have been conducted to come to conclusion as to which grain configuration is ideal for the current design requirement under study.

• 한국자동차공학회논문집
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• 제19권1호
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• pp.66-72
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• 2011

Injection pressure is an important factor in filling procedure for injection molded parts. In addition, weldlines should be avoided to successfully produce injection molded parts. In this study, we optimally obtained injection molding process parameters that minimize injection pressure. Then, we determined the thickness of the part to avoid weldlines. To solve the optimization problem proposed, we employed MAPS-3D (Mold Analysis and Plastics Solution-3 Dimension), a commercial CAE tool for injection molding analysis, and PIAnO (Process Integration, Automation, and Optimization) as a commercial PIDO (Process Integration and Design Optimization) tool. We integrated MAPS-3D into PIAnO, automated the analysis and design procedure, and performed optimization by employing PQRSM (Progressive Quadratic Response Surface Method) equipped in PIAnO. We successfully obtained optimization results, which demonstrates the effectiveness of our design method.

• Nuclear Engineering and Technology
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• 제55권6호
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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.