The finite element method (FEM) has been extensively applied to explore contact stress distributions in multi-body mechanical systems. Uneven contact stresses are often one of the main concerns of mechanical design en...
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The finite element method (FEM) has been extensively applied to explore contact stress distributions in multi-body mechanical systems. Uneven contact stresses are often one of the main concerns of mechanical design engineers. By adopting the evolutionary structural optimization (ESO) concept, this paper presents a non-gradient procedure for gradual shape redesign of prescribed contact interfaces. In this method, interfacial gaps are considered as design variables and contact stress deviations over design interfaces are set as the objective function. To deal with multiple contact region problems, two different design objectives, namely an individual criteria and a unified criteria, are formulated respectively. Several practical examples show that this method is effective for design problems consisting of single- or multiple-contact regions in mechanical systems, in which a uniform contact stress pattern is the desired optimality criterion.
This paper proposes a multi-objective evolutionary automated design methodology for multi-variable quantitative feedback theory (QFT) control systems. Unlike existing analytical and convex optimization-based QFT desig...
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This paper proposes a multi-objective evolutionary automated design methodology for multi-variable quantitative feedback theory (QFT) control systems. Unlike existing analytical and convex optimization-based QFT design approaches, the evolutionary 'intelligent' technique is capable of automatically evolving both the nominal controller and the pre-filter simultaneously to meet the usually conflicting multiple performance requirements in QFT, without going through the sequential and conservative design stages for each of the multi-variable subsystems. In addition, it avoids the need of manual QFT bound computation and trial-and-error loop-shaping design procedures, which are particularly useful for multi-variable or unstable plants where stabilizing controllers may be difficult to synthesize. The effectiveness of the proposed QFT design methodology is validated upon a benchmark multi-variable system, which offers a set of low-order Pareto optimal controllers satisfying all closed-loop performances under practical constraints.
Decision-based design (DBD) attempts to integrate an array of disparate elements into a system-level analytical tool. This paper presents some practical and easily implemented approaches for a design optimization syst...
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Inverse shape design and shape design optimization are two basic algorithmic approaches to aerodynamic shape determination. Although widely used in industry, most inverse shape design methods require significant modif...
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This paper proposes a multi-objective evolutionary automated design methodology for multivariable QFT control systems. Unlike existing manual or convex optimisation based QFT design approaches, the 'intelligent...
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ISBN:
(纸本)3540673539
This paper proposes a multi-objective evolutionary automated design methodology for multivariable QFT control systems. Unlike existing manual or convex optimisation based QFT design approaches, the 'intelligent' evolutionary technique is capable of automatically evolving both the nominal controller and pre-filter simultaneously to meet all performance requirements in QFT, without going through the conservative and sequential design stages for each of the multivariable sub-systems. In addition, it avoids the need of manual QFT bound computation and trial-and-error loop-shaping design procedures, which is particularly useful for unstable or non-minimum phase plants for which stabilising controllers maybe difficult to be synthesised. Effectiveness of the proposed QFT design methodology is validated upon a benchmark multivariable system, which offers a set of low-order Pareto optimal controllers that satisfy all the required closed-loop performances under practical constraints.
CAE technology has been applied for the crash safety design of vehicles, however, the optimum design has not been completed for the crash behavior of the vehicles. The authors have proposed a Statistical Design Suppor...
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CAE technology has been widely applied for the crash safety design of vehicles. Nevertheless, an optimum design approach has not been completed for the crash behavior of the vehicles because of the nonlinearity of the...
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CAE technology has been widely applied for the crash safety design of vehicles. Nevertheless, an optimum design approach has not been completed for the crash behavior of the vehicles because of the nonlinearity of the dynamic problem. The authors have proposed a Statistical Design Support System (SDSS), and suggested this is one of the available optimal approaches for nonlinear and dynamic problems such as the vehicles. In this study, the SDSS was applied for the multi-objectiveoptimization design of the main reinforced members of a vehicle. The thicknesses of each member were chosen as the design variables, and the total weight and the deformation of the cabin as the multi-objective functions. It was shown that the SDSS could satisfactorily be used as a practicalmulti-objectiveoptimization design tool for the crash safety design of vehicles, and the design cycle could be significantly reduced using SDSS.
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