• International Journal of Automotive Technology
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• v.4 no.3
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• pp.135-140
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• 2003

In the deterministic optimization of a structural system, objective function, design constraints and design variables, are treated in a nonstatistical fashion. However, such deterministic engineering optimization tends to promote the structural system with lest reliability redundancy than obtained with conventional design procedures using the factor of safety. Consequently, deterministic optimized structures will usually have higher failure probabilities than unoptimized structures. Therefore, a balance must be developed between the satisfactions of the design requirements and the objectives of reducing manufacturing cost. This paper proposes the reliability-based design optimization (RBDO) technique, which enables the optimum design that considers confidence level for the vibration characteristics of structural system. Response surface method (RSM) is utilized to approximate the performance functions describing the system characteristics in the RBDO procedure. The proposed optimization technique is applied to the pillar section design considering natural frequencies of a vehicle structure.

• Journal of Ocean Engineering and Technology
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• v.26 no.6
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• pp.80-85
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• 2012

The methods of robust design optimization (RDO) and reliability-based robust design optimization (RBRDO) were implemented in the present study. RBRDO is an integrated method that accounts for the design robustness of an objective function and for the reliability of constraints. The objective function in RBRDO is expressed in terms of the mean and standard deviation of an original objective function. Thus, a multi-objective formulation is employed. The regressive approximate models are generated via the moving least squares method (MLSM) and constraint-feasible moving least squares method (CF-MLSM), which make it possible to realize the feasibility regardless of the multimodality/nonlinearity of the constraint function during the approximate optimization processes. The regression model based RBRDO is newly devised and its numerical characteristics are explored using the design of an actively controlled ten bar truss structure.

• Journal of Magnetics
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• v.17 no.1
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• pp.46-50
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• 2012

A reliability-based optimization method for electromagnetic design is presented to take uncertainties of design parameters into account. The method can provide an optimal design satisfying a specified confidence level in the presence of uncertain parameters. To achieve the goal, the reliability index approach based on the firstorder reliability method is adopted to deal with probabilistic constraint functions and a double-loop optimization algorithm is implemented to obtain an optimum. The proposed method is applied to the TEAM Workshop Problem 22 and its accuracy and efficiency is verified with reference of Monte Carlo simulation results.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.225-234
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• 2020

Reliability-based structural optimization usually requires designers or engineers model different designs manually, which is considered very time consuming and all possibilities cannot be fully explored. Otherwise, a lot of time are needed for designers or engineers to learn mathematical modeling and programming skills. Therefore, a framework that integrates generative design, structural simulation and reliability theory is proposed. With the proposed framework, various designs are generated based on a set of rules and parameters defined based on visual programming, and their structural performance are simulated by OpenSees. Then, reliability of each design is evaluated based on the simulation results, and an optimal design can be found. The proposed framework and prototype are tested in the optimization of a steel frame structure, and results illustrate that generative design based on visual programming is user friendly and different design possibilities can be explored in an efficient way. It is also reported that structural reliability can be assessed in an automatic way by integrating Dynamo and OpenSees. This research contributes to the body of knowledge by providing a novel framework for automatic reliability evaluation and structural optimization.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.2
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• pp.123-130
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• 2004

The automobile suspension system is composed of parts that affect performances of a vehicle such as ride quality, handling characteristics, straight performance and steering effort, etc. Moreover, by using the finite element analysis the cost for the initial design step can be decreased. In the design of a suspension system, usually system vibration and structural rigidity must be considered simultaneously to satisfy dynamic and static requirements simultaneously. In this paper, we consider the weight reduction and the increase of the first eigen-frequency of a suspension part, the upper control arm, especially using topology optimization and size optimization. Firstly, we obtain the initial design to maximize the first eigen-frequency using topology optimization. Then, we apply the multi-objective parameter optimization method to satisfy both the weight reduction and the increase of the first eigen-frequency. The design variables are varying during the optimization process for the multi-objective. Therefore, we can obtain the deterministic values of the design variables not only to satisfy the terms of variation limits but also to optimize the two design objectives at the same time. Finally, we have executed reliability based optimal design on the upper control arm using the Monte-Carlo method with importance sampling method for the optimal design result with 98％ reliability.

• International Journal of Automotive Technology
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• v.8 no.1
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• pp.93-102
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• 2007

The frontal crash optimization of an engine room member using the response surface method was studied. The engine room member is composed of the front side member and the sub-frame. The thicknesses of the panels on the front side member and the sub-frame were selected as the design variables. The purpose of the optimization was to reduce the weight of the structure, under the constraint that the objective quantity of crash energy is absorbed. The response surface method was used to approximate the crash behavior in mathematical form for optimization procedure. To research the effect of the regression method, two different methodologies were used in constructing the response surface model, the least square method and the moving least square method. The optimum with the two methods was verified by the simulation result. The precision of the surrogate model affected the optimal design. The moving least square method showed better approximation than the least square method. In addition to the deterministic optimization, the reliability-based design optimization using the response surface method was executed to examine the effect of uncertainties in design variables. The requirement for reliability made the optimal structure be heavier than the result of the deterministic optimization. Compared with the deterministic optimum, the optimal design using the reliability-based design optimization showed higher crash energy absorption and little probability of failure in achieving the objective.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.672-676
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• 2002

In this paper, a reliability-based design optimization method, which enables the determination of optimum design that incorporate confidence range for structures, is studied. Response surface method and Monte Carlo simulation are utilized to determine limit state function. The proposed method is applied to the I-type steel structure for reliability based optimal design.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.22 no.4
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• pp.299-306
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• 2009

Design optimization is a method to find optimum point which minimizes the objective function while satisfying design constraints. The conventional optimization does not consider the uncertainty originated from modeling or manufacturing process, so optimum point often locates on the boundaries of constraints. Reliability based design optimization includes optimization technique and reliability analysis that calculates the reliability of the system. Reliability analysis can be classified into simulation method, fast probability integration method, and moment-based reliability method. In most generally used MPP based reliability analysis, which is one of fast probability integration method, if many MPP points exist, cost and numerical error can increase in the process of transforming constraints into standard normal distribution space. In this paper, multiplicative decomposition method is used as a reliability analysis for RBDO, and sensitivity analysis is performed to apply gradient based optimization algorithm. To illustrate whole process of RBDO mathematical and engineering examples are illustrated.

• Journal of computational fluids engineering
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• v.17 no.2
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• pp.28-34
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• 2012

In this study, the reliability-based design optimization of the airfoil was performed. PARSEC function was used to consider the uncertainty of the aerodynamic shape for the reliability-based shape optimization of airfoils. Among various reliability analysis methods, the moment method was used to compute the probability of failure of the aerodynamic performance. The accuracy of the reliability analysis was compared with other methods and it was found that the moment method predicts the probability of failure accurately. Deterministic and reliability-based optimizations were performed for the shape of the airfoil and it was demonstrated that reliability-based optimum assures the aerodynamic performances under uncertainties of the shape of the airfoil.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.10a
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• pp.101-108
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• 2000

An optimum design algorithm using efficient reanalysis is proposed for reliability-based optimization problems formulated as the minimization of initial cost and expected failure cost with reliability constraints. The reliability-based optimization is high cost to evaluate objective function and constraints needed reliability analysis. Therefore the sensitivity analysis of reliability index for approximated reanalysis is necessary. In this paper, three solution approaches are suggested and tested. The approaches include : (1) sensitivity analysis using finite difference; (2) sensitivity analysis using automatic differentiation (AD); and (3) sensitivity analysis with respect to intermediate variables using AD. Numerical example is optimized to show the reliability and effectiveness of the new algorithm.