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

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.33 no.8
• /
• pp.726-732
• /
• 2009

In many industries, the linear motor replaces the existing framework for linear transportation. Similar to other conventional motors, it is important to minimize the ripple of thrust and to maximize the thrust force of the linear motor. Because the two objectives are associated to each other, the multi-objective design process is necessary considering all objectives. This paper intends to optimize geometric parameters of the linear motor with two design objectives using design of experiments and sequential response surface method.

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

• Journal of Computational Design and Engineering
• /
• v.2 no.3
• /
• pp.157-164
• /
• 2015

This paper discusses an optimization-based approach for the design of a product platform for industrial three-axis linear-type robots, which are widely used for handling objects in manufacturing lines. Since the operational specifications of these robots, such as operation speed, working distance and orientation, weight and shape of loads, etc., will vary for different applications, robotic system vendors must provide various types of robots efficiently and effectively to meet a range of market needs. A promising step toward this goal is the concept of a product platform, in which several key elements are commonly used across a series of products, which can then be customized for individual requirements. However the design of a product platform is more complicated than that of each product, due to the need to optimize the design across many products. This paper proposes an optimization-based fundamental framework toward the design of a product platform for industrial three-axis linear-type robots; this framework allows the solution of a complicated design problem and builds an optimal design method of fundamental features of robot frames that are commonly used for a wide range of robots. In this formulation, some key performance metrics of the robot are estimated by a reducedorder model which is configured with beam theory. A multi-objective optimization problem is formulated to represent the trade-offs among key design parameters using a weighted-sum form for a single product. This formulation is integrated into a mini-max type optimization problem across a series of robots as an optimal design formulation for the product platform. Some case studies of optimal platform design for industrial three-axis linear-type robots are presented to demonstrate the applications of a genetic algorithm to such mathematical models.

• Earthquakes and Structures
• /
• v.7 no.3
• /
• pp.271-294
• /
• 2014

In recent years, along with the advances made in performance-based design optimization, the need for fast calculation of response parameters in dynamic analysis procedures has become an important issue. The main problem in this field is the extremely high computational demand of time-history analyses which may convert the solution algorithm to illogical ones. Two simplifying strategies have shown to be very effective in tackling this problem; first, simplified nonlinear modeling investigating minimum level of structural modeling sophistication, second, wavelet analysis of earthquake records decreasing the number of acceleration points involved in time-history loading. In this paper, we try to develop an efficient framework, using both strategies, to solve the performance-based multi-objective optimal design problem considering the initial cost and the seismic damage cost of steel moment-frame structures. The non-dominated sorting genetic algorithm (NSGA-II) is employed as the optimization algorithm to search the Pareto optimal solutions. The constraints of the optimization problem are considered in accordance with Federal Emergency Management Agency (FEMA) recommended design specifications. The results from numerical application of the proposed framework demonstrate the capabilities of the framework in solving the present multi-objective optimization problem.

• Structural Engineering and Mechanics
• /
• v.86 no.2
• /
• pp.249-260
• /
• 2023

The uncertainties involved in structural performances are of importance when the optimum number and property of seismic retrofit devices are determined. This paper proposes a seismic retrofit design framework for asymmetric soft-first-story buildings, considering uncertainties in the soil condition and seismic retrofit device. The effect of the uncertain parameters on the structural performance is used to find a robust and optimal seismic retrofit solution. The framework finds a robust and optimal seismic retrofit solution by finding the optimal locations and mechanical properties of the seismic retrofit device for different realizations of the uncertain parameters. The structural performance for each realization is computed to evaluate the effect of the uncertainty parameters on the seismic performance. The framework utilizes parallel processing to decrease the computationally intensive nonlinear dynamic analysis time. The framework returns a robust design solution that satisfies the given limit state for every realization of the uncertain parameters. The proposed framework is applied to the seismic retrofit design of a five-story asymmetric soft-first-story case study structure retrofitted with a viscoelastic damper. Robust optimal parameters for retrofitting a structure to satisfy the limit state for the different realizations of the uncertain parameter are found using the proposed framework. According to the performance evaluation results of the retrofitted structure, the developed framework is proved effective in the seismic retrofit of the asymmetric structure with inherent uncertainties.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.47 no.6
• /
• pp.460-467
• /
• 2019

This paper proposes a multi-objective optimization (MOO) framework for conceptual design of a surface-to-surface missile system. It can generate the set of Pareto optimal system design, which can be used for system trade-off study in a very early stage of the research and development process. The proposed framework consists of four functional modules (an environmental setting module, a variable setting module, a multidisciplinary analysis module and an optimization module) to make the model easy to change, and the concept design process using the framework was able to achieve the purpose of reviewing various designs in the early stage of development. A case study demonstrating the effectiveness of the framework has presented applicability to the system design, and the proposed framework has contributed to presenting a design environment that can ensure reliability and reduce computational time in the conceptual design stage.

• Structural Engineering and Mechanics
• /
• v.47 no.2
• /
• pp.227-245
• /
• 2013

The term "constructability" in regard to cast-in-place concrete construction refers mainly to the ease of reinforcing steel placement. Bar congestion complicates steel placement, hinders concrete placement and as a result leads to improper consolidation of concrete around bars affecting the integrity of the structure. In this paper, a multi-objective approach, based on the non-dominated sorting genetic algorithm (NSGA-II) is developed for optimal design of reinforced concrete cantilever retaining walls, considering minimization of the economic cost and reinforcing bar congestion as the objective functions. The structural model to be optimized involves 35 design variables, which define the geometry, the type of concrete grades, and the reinforcement used. The seismic response of the retaining walls is investigated using the well-known Mononobe-Okabe analysis method to define the dynamic lateral earth pressure. The results obtained from numerical application of the proposed framework demonstrate its capabilities in solving the present multi-objective optimization problem.

• Advances in aircraft and spacecraft science
• /
• v.10 no.3
• /
• pp.245-256
• /
• 2023

In order to improve aerodynamic performance of multi-stage axial flow turbines used in aircraft engines, a one-dimensional aerodynamic design and optimization framework is constructed. In the method, flow path is generated by solving mass continuation and energy conservation with loss computed by the Craig & Cox model; Also real gas properties has been taken into consideration. To obtain an optimal result, a multi-objective genetic algorithm is used to optimize the efficiencies and determine values of various design variables; Final design can be selected from obtained Pareto optimal solution sets. A three-stage axial turbine is used to verify the effectiveness of the developed optimization framework, and designs are checked by three-dimensional CFD simulation. Results show that the aerodynamic performance of the optimized turbine has been significantly improved at design point, with the total-to-total efficiency increased by 1.17% and the total-to-static efficiency increased by 1.48%. As for the off-design performance, the optimized one is improved at all working points except those at small mass flow.

• Journal of Mechanical Science and Technology
• /
• v.19 no.5
• /
• pp.1080-1086
• /
• 2005

The preliminary design optimization of multi-stage spur gear reduction units has been a subject of considerable interest, since many high-performance power transmission applications (e.g., automotive and aerospace) require high-performance gear reduction units. There are multiple objectives in the optimal design of multi-stage spur gear reduction unit, such as minimizing the volume and maximizing the surface fatigue life. It is reasonable to formulate the design of spur gear reduction unit as a multi-objective optimization problem, and find an appropriate approach to solve it. In this paper an interactive physical programming approach is developed to place physical programming into an interactive framework in a natural way. Class functions, which are used to represent the designer's preferences on design objectives, are fixed during the interactive physical programming procedure. After a Pareto solution is generated, a preference offset is added into the class function of each objective based on whether the designer would like to improve this objective or sacrifice the objective so as to improve other objectives. The preference offsets are adjusted during the interactive physical programming procedure, and an optimal solution that satisfies the designer's preferences is supposed to be obtained by the end of the procedure. An optimization problem of three-stage spur gear reduction unit is given to illustrate the effectiveness of the proposed approach.