• International Journal of Aeronautical and Space Sciences
• /
• v.17 no.1
• /
• pp.20-28
• /
• 2016

In this study, an efficient method of designing laminate composite radar absorbing structures (RAS) is proposed with consideration given to the structural shape so as to improve aircraft stealth performance. The calculation of the radar cross section (RCS) should be decreased to enhance the efficiency of the stochastic optimization when designing an RAS. In the proposed method, RAS are optimized to match up the input impedance of the minimal RCS, which is obtained by using physical optics and the transmission line theory. Single and double layer dielectric RAS for aircraft wings are employed as numerical examples and designed using the proposed method, RCS minimization and reflection coefficient minimization. The availability of the proposed method is assessed by comparing the similarity of the results and computation time with other design methods. According to the results, the proposed method produces the same results as the stochastic optimization, which adopts the RCS as the objective function, and can improve RAS design efficiency by reducing the number of RCS analyses.

• Structural Engineering and Mechanics
• /
• v.46 no.1
• /
• pp.19-37
• /
• 2013

The optimum design of base isolation system considering model parameter uncertainty is usually performed by using the unconditional response of structure obtained by the total probability theory, as the performance index. Though, the probabilistic approach is powerful, it cannot be applied when the maximum possible ranges of variations are known and can be only modelled as uncertain but bounded type. In such cases, the interval analysis method is a viable alternative. The present study focuses on the bounded optimization of base isolation system to mitigate the seismic vibration effect of structures characterized by bounded type system parameters. With this intention in view, the conditional stochastic response quantities are obtained in random vibration framework using the state space formulation. Subsequently, with the aid of matrix perturbation theory using first order Taylor series expansion of dynamic response function and its interval extension, the vibration control problem is transformed to appropriate deterministic optimization problems correspond to a lower bound and upper bound optimum solutions. A lead rubber bearing isolating a multi-storeyed building frame is considered for numerical study to elucidate the proposed bounded optimization procedure and the optimum performance of the isolation system.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.33 no.8
• /
• pp.745-752
• /
• 2009

In robust design, the mean and variance of design performance are frequently used to measure the design performance and its robustness under uncertainties. In this paper, we present the Gauss-type quadrature formula as a rigorous method for mean and variance estimation involving arbitrary input distributions and further extend its use to robust design optimization. One dimensional Gauss-type quadrature formula are constructed from the input probability distributions and utilized in the construction of multidimensional quadrature formula such as the tensor product quadrature (TPQ) formula and the univariate dimension reduction (UDR) method. To improve the efficiency of using it for robust design optimization, a semi-analytic design sensitivity analysis with respect to the statistical moments is proposed. The proposed approach is applied to a simple bench mark problems and robust topology optimization of structures considering various types of uncertainty.

• Smart Structures and Systems
• /
• v.24 no.6
• /
• pp.733-744
• /
• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Journal of Mechanical Science and Technology
• /
• v.18 no.4
• /
• pp.630-639
• /
• 2004

The Stewart platform manipulator is a closed-kinematics chain robot manipulator that is capable of providing high structural rigidity and positional accuracy. However, this is a complex and nonlinear system, so the control performance of the system is not so good. In this paper, a new robust motion control algorithm is proposed. The algorithm uses partial state feedback for a class of nonlinear systems with modeling uncertainties and external disturbances. The major contribution is the design of a robust observer for the state and the perturbation of the Stewart platform, which is combined with a variable structure controller (VSC). The combination of controller and observer provides the robust routine called sliding mode control with sliding perturbation observe. (SMCSPO). The optimal gains of SMCSPO, which is determined by nominal eigenvalues, are easily obtained by genetic algorithm. The proposed fitness function that evaluates the gain optimization is to put sliding function. The control performance of the proposed algorithm is evaluated by the simulation and experiment to apply to the Stewart platform. The results showed high accuracy and good performance.

• International journal of steel structures
• /
• v.18 no.5
• /
• pp.1598-1606
• /
• 2018

An integrated optimal structural design method for a diagrid structure and control device was developed. A multi-objective genetic algorithm was used and a 60-story diagrid building structure was developed as an example structure. Artificial wind and earthquake loads were generated to assess the wind-induced and seismic responses. A smart tuned mass damper (TMD) was used as a structural control system and an MR (magnetorheological) damper was employed to develop a smart TMD (STMD). The multi-objective genetic algorithm used five objectives including a reduction of the dynamic responses, additional stiffness and damping, mass of STMD, capacity of the MR damper for the integrated optimization of a diagrid structure and a STMD. From the proposed method, integrated optimal designs for the diagrid structure and STMD were obtained. The numerical simulation also showed that the STMD provided good control performance for reducing the wind-induced and seismic responses of a tall diagrid building structure.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.26 no.1
• /
• pp.69-77
• /
• 2013

When structural design of a tall building is conducted, reduction of wind-induced lateral displacement is one of the most important problem. For this purpose, additional dampers and vibration control devices are generally considered. In this process, control performance of additional devices are usually investigated for optimal design without variation of characteristics of a structure. In this study, multi-objective integrated optimization of structure-smart control device is conducted and possibility of reduction of structural resources of a tall building with additional smart damping device has been investigated. To this end, a 60-story diagrid building structure is used as an example structure and artificial wind loads are used for evaluation of wind-induced responses. An MR damper is added to the conventional TMD to develop a smart TMD. Because dynamic responses and the amount of structural material and additional smart damping devices are required to be reduced, a multi-objective genetic algorithm is employed in this study. After numerical simulation, various optimal designs that can satisfy control performance requirement can be obtained by appropriately reducing the amount of structural material and additional smart damping device.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.13 no.1
• /
• pp.119-125
• /
• 2005

The current automotive is seeking the improvement of performance, the prevention of environmental pollution and the saving of energy resources according to miniaturization and lightweight of the components. And the variance analysis on the basis of structure analysis and DOE is applied to the lower control am. We have proposed a statistical design model to evaluate the effect of structural modification by performing the practical multi-objective optimization considering weight, stress and fatigue lift. The lower control arm is performed the fatigue analysis using the load history of real road test. The design model is determined using the optimization of acquired load history with the fatigue characteristic. The characteristic function is made use of the optimization according to fatigue characteristics to consider constrained function in the optimization of DOE. The structure optimization of a lower control arm according to fatigue characteristics is performed. And the optimized design variable is D=47 m, T=36mm, W=12 mm. In the real engineering problem of considering many objective functions, the multi-objective optimization process using the mathematical programming and the characteristic function is derived an useful design solution.

• Advances in Computational Design
• /
• v.9 no.2
• /
• pp.115-136
• /
• 2024

Stochastic optimization methods have been extensively studied for structural optimization in recent decades. In this study, a novel algorithm named the CA-SA method, is proposed for topology optimization of steel double-layer grid structures. The CA-SA method is a hybridized algorithm combining the Simulated Annealing (SA) algorithm and the Cellular Automata (CA) method. In the CA-SA method, during the initial iterations of the SA algorithm, some of the preliminary designs obtained by SA are placed in the cells of the CA. In each successive iteration, a cell is randomly chosen from the CA. Then, the "local leader" (LL) is determined by selecting the best design from the chosen cell and its neighboring ones. This LL then serves as the leader for modifying the SA algorithm. To evaluate the performance of the proposed CA-SA algorithm, two square-on-square steel double-layer grid structures are considered, with discrete cross-sectional areas. These numerical examples demonstrate the superiority of the CA-SA method over SA, and other meta-heuristic algorithms reported in the literature in the topology optimization of large-scale skeletal structures.

• Smart Structures and Systems
• /
• v.2 no.3
• /
• pp.253-274
• /
• 2006

The structural monitoring of multi-wire strands is of importance to prestressed concrete structures and cable-stayed or suspension bridges. This paper addresses the monitoring of strands by ultrasonic guided waves with emphasis on the signal processing and automatic defect classification. The detection of notch-like defects in the strands is based on the reflections of guided waves that are excited and detected by magnetostrictive ultrasonic transducers. The Discrete Wavelet Transform was used to extract damage-sensitive features from the detected signals and to construct a multi-dimensional Damage Index vector. The Damage Index vector was then fed to an Artificial Neural Network to provide the automatic classification of (a) the size of the notch and (b) the location of the notch from the receiving sensor. Following an optimization study of the network, it was determined that five damage-sensitive features provided the best defect classification performance with an overall success rate of 90.8%. It was thus demonstrated that the wavelet-based multidimensional analysis can provide excellent classification performance for notch-type defects in strands.