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  • 2000-2004  (2)
  • Key words: permutation genetic algorithms, composite laminate optimization, response surfaces, two-level optimization, wing design, combinatorial optimization  (1)
  • passive strategy  (1)
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  • 2000-2004  (2)
Year
Keywords
  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Structural and multidisciplinary optimization 20 (2000), S. 87-96 
    ISSN: 1615-1488
    Keywords: Key words: permutation genetic algorithms, composite laminate optimization, response surfaces, two-level optimization, wing design, combinatorial optimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract. A two-level optimization procedure for composite wing design subject to strength and buckling constraints is presented. At wing-level design, continuous optimization of ply thicknesses with orientations of 0°, 90°, and ±45° is performed to minimize weight. At panel level, the number of plies of each orientation (rounded to integers) and inplane loads are specified, and a permutation genetic algorithm is used to optimize the stacking sequence in order to maximize the buckling load. The process is started by performing a large number of panel genetic optimizations for a range of loads and numbers of plies of each orientation. Next, a cubic polynomial response surface is fitted to the optimum buckling load as a function of the loads and numbers of plies of each orientation. The resulting response surface is used for the wing-level optimization. Rounding and manual adjustment are used to obtain the final design. The procedure is demonstrated using an example of a simple wing box design.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Journal of optimization theory and applications 105 (2000), S. 189-212 
    ISSN: 1573-2878
    Keywords: learning ; estimation ; monitoring ; industrial processes ; kernel function ; passive strategy ; stochastic approximations
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract A class of estimation/learning algorithms using stochastic approximation in conjunction with two kernel functions is developed. This algorithm is recursive in form and uses known nominal values and other observed quantities. Its convergence analysis is carried out; the rate of convergence is also evaluated. Applications to a nonlinear chemical engineering system are examined through simulation study. The estimates obtained will be useful in process operation and control, and in on-line monitoring and fault detection.
    Type of Medium: Electronic Resource
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