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Optimization Methodologies (optimization + methodology)

Distribution by Scientific Domains

Engineering	75%

Selected Abstracts

Reducing dimensionality in topology optimization using adaptive design variable fields

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 8 2010
James K. Guest
Abstract Topology optimization methodologies typically use the same discretization for the design variable and analysis meshes. Analysis accuracy and expense are thus directly tied to design dimensionality and optimization expense. This paper proposes leveraging properties of the Heaviside projection method (HPM) to separate the design variable field from the analysis mesh in continuum topology optimization. HPM projects independent design variables onto element space over a prescribed length scale. A single design variable therefore influences several elements, creating a redundancy within the design that can be exploited to reduce the number of independent design variables without significantly restricting the design space. The algorithm begins with sparse design variable fields and adapts these fields as the optimization progresses. The technique is demonstrated on minimum compliance (maximum stiffness) problems solved using continuous optimization and genetic algorithms. For the former, the proposed algorithm typically identifies solutions having objective functions within 1% of those found using full design variable fields. Computational savings are minor to moderate for the minimum compliance formulation with a single constraint, and are substantial for formulations having many local constraints. When using genetic algorithms, solutions are consistently obtained on mesh resolutions that were previously considered intractable. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Simulation and optimal design of multiple-bed pressure swing adsorption systems

AICHE JOURNAL, Issue 11 2004
Ling Jiang
Abstract Pressure swing adsorption (PSA) is a very versatile technology for gas separation and purification. The widespread industrial application of PSA has called for an efficient set of simulation, design, and optimization methodologies. In previous work by Jiang and co-workers, we used a Newton-based approach to quickly converge the cyclic steady state and design constraints, and a simultaneous tailored approach with the state-of-art nonlinear optimization strategy to design optimal PSA processes. In this work we extend the simulation and optimization strategies to multiple bed systems. Both unibed and multibed frameworks are adopted to describe bed behaviors. The unibed framework models only one bed over a cycle and uses storage buffers to mimic the bed interactions. The multibed framework simultaneously solves all beds but only for a portion of the cycle. Challenges and implementation details of both frameworks are discussed. A five-bed, 11-step hydrocarbon separation process, which separates H2 from a mixture of H2, N2, CO2, CO, and CH4, is used for illustration. By manipulating valve constants, step times, flow rates, and bed geometry, the optimizer successfully maximizes H2 recovery, while meeting product purity and pressure specifications. © 2004 American Institute of Chemical Engineers AIChE J, 50: 2904,2917, 2004 [source]

Methodology for the optimal component selection of electronic devices under reliability and cost constraints

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 8 2007
E. P. Zafiropoulos
Abstract The objective of this paper is to present an efficient computational methodology for the reliability optimization of electronic devices under cost constraints. The system modeling for calculating the reliability indices of the electronic devices is based on Bayesian networks using the fault tree approach, in order to overcome the limitations of the series,parallel topology of the reliability block diagrams. Furthermore, the Bayesian network modeling for the reliability analysis provides greater flexibility for representing multiple failure modes and dependent failure events, and simplifies fault diagnosis and reliability allocation. The optimal selection of components is obtained using the simulated annealing algorithm, which has proved to be highly efficient in complex optimization problems where gradient-based methods can not be applied. The reliability modeling and optimization methodology was implemented into a computer program in Matlab using a Bayesian network toolbox. The methodology was applied for the optimal selection of components for an electrical switch of power installations under reliability and cost constraints. The full enumeration of the solution space was calculated in order to demonstrate the efficiency of the proposed optimization algorithm. The results obtained are excellent since a near optimum solution was found in a small fraction of the time needed for the complete enumeration (3%). All the optimum solutions found during consecutive runs of the optimization algorithm lay in the top 0.3% of the solutions that satisfy the reliability and cost constraints. Copyright © 2007 John Wiley & Sons, Ltd. [source]

State feedback control synthesis for networked control systems with packet dropout,

ASIAN JOURNAL OF CONTROL, Issue 1 2009
Yu-Long Wang
Abstract This paper is concerned with the problem of H, controller design for networked control systems (NCSs) with time delay and packet dropout. A combined switching and parameter uncertainty-based method is proposed to deal with time-varying delay. The proposed method can avoid the high computational complexity of the delay switching-based method and introduce less conservatism than the parameter uncertainty-based method. An active varying sampling period method is proposed to make full use of network bandwidth, and a multi-objective optimization methodology in terms of linear matrix inequalities is used to deal with H, controller design for NCSs with active varying sampling period. The simulation results illustrate the effectiveness of the proposed active varying sampling period method and the less conservatism of the combined switching and parameter uncertainty-based method. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]