Adaptive Response Surface Method Using Inherited Latin Hypercube Design Points

Wang, G. Gary

doi:10.1115/1.1561044

Cited by 447 publications

(199 citation statements)

References 41 publications

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“…In that case, the engineers must manually adjust variables to an appropriate number, deviating from one of the defined levels. Thus the property of OA might be undermined [15]. Therefore for this algorithm LHD is used as DOE method.…”

Section: Design Of Experimentsmentioning

confidence: 99%

Smart Response Surface Models using Legacy Data for Multidisciplinary Optimization of Aircraft Systems

Gabbur¹

2017

IOSR JMCE

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Section: Design Of Experimentsmentioning

confidence: 99%

Smart Response Surface Models using Legacy Data for Multidisciplinary Optimization of Aircraft Systems

Gabbur¹

2017

IOSR JMCE

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“…This induces a high-dimensional neural architecture, and then requires a large-sized learning base to perform training. Yet, a mesh-based training base with more than 4 8 examples cannot be considered. Therefore we build our training bases using regular meshing on loading variables crossed with a latin-hypercube sampling (LHS) on design variables.…”

Section: Design Of Experimentsmentioning

confidence: 99%

Application of Response Surface Methodology to Stiffened Panel Optimization

Merval¹,

Samuelides²,

Grihon³

2006

47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

7
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In a multilevel optimization frame, the use of surrogate models to approximate optimization constraints allows great time saving. Among available metamodelling techniques we chose to use Neural Networks to perform regression of static mechanical criteria, namely buckling and collapse reserve factors of a stiffened panel, which are constraints of our subsystem optimization problem. Due to the highly non linear behaviour of these functions with respect to loading and design variables, we encountered some difficulties to obtain an approximation of sufficient quality on the whole design space. In particular, variations of the approximated function can be very different according to the value of loading variables. We show how a prior knowledge of the influence of the variables allows us to build an efficient Mixture of Expert model, leading to a good approximation of constraints. Optimization benchmark processes are computed to measure time saving, effects on optimum feasibility and objective value due to the use of the surrogate models as constraints. Finally we see that, while efficient, this mixture of expert model could be still improved by some additional learning techniques. I. Position of the problem in the multilevel optimization chainAeronautical structures are mainly made of stiffened panels, i.e. thin shells (also called skin) enforced with stiffeners (respectively called frames and stringers) in both orbital and longitudinal directions (see figure 1). For the sake of the study the whole structure is divided into elementary parts called Super Stiffeners, consisting in the theoretically union of a stringer and two half panels. These basic structures are subject to highly non linear phenomena such as buckling, collapse and damage tolerance.In order to determine the optimal size of these super stiffeners, static mechanical criteria must be computed using dedicated software that is based on non linear calculation. Thus, the analysis and the dimension estimation of the whole structure is currently computed by running a two-level study: at a global level, a Finite Element (FE) analysis run on the whole FE model provides internal loads -applied to each S-Stiffener; at a local level, these loads are used to compute static mechanical criteria. Most of these criteria are formulated using Reserve Factors (RF): a structure is validated provided all its RFs are greater than 1.So, detailed design of a aircraft fuselage requires a two-level loop: first, numerous local optimizations are run on isolated super stiffeners in order to size them respecting mechanical criteria, depending on current internal loads. But changes in local geometry from initial to optimal design involve a new load distribution in the whole structure; an update step must then be performed to take these changes into account. This bilevel optimization process is then repeated until convergence of load distribution in the whole structure. Nowadays, this loose coupling process depicted in figure 2 is practically implemented in real industrial case. Other...

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“…For example, Wang et al (2001Wang et al ( , 2003 developed the Adaptive Response Surface Method (ARSM), which disregarded regions with large function values as predicted by the surrogate, and built a new DOE using central composite design or Latin Hypercube sampling (LHS) in the reduced region. The mode-pursuing sampling method (Wang et al 2004) creates local quadratic surrogates for promising regions, where the sampling approach tends to generate dense samples.…”

Section: Global-local Approachesmentioning
confidence: 99%

Parallel surrogate-assisted global optimization with expensive functions – a survey
Haftka
¹
,
Villanueva
²
,
Chaudhuri
³

2016
Struct Multidisc Optim
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Surrogate assisted global optimization is gaining popularity. Similarly, modern advances in computing power increasingly rely on parallelization rather than faster processors. This paper examines some of the methods used to take advantage of parallelization in surrogate based global optimization. A key issue focused on in this review is how different algorithms balance exploration and exploitation. Most of the papers surveyed are adaptive samplers that employ Gaussian Process or Kriging surrogates. These allow sophisticated approaches for balancing exploration and exploitation and even allow to develop algorithms with calculable rate of convergence as function of the number of parallel processors. In addition to optimization based on adaptive sampling, surrogate assisted parallel evolutionary algorithms are also surveyed. Beyond a review of the present state of the art, the paper also argues that methods that provide easy parallelization, like multiple parallel runs, or methods that rely on population of designs for diversity deserve more attention.

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Adaptive Response Surface Method Using Inherited Latin Hypercube Design Points

Cited by 447 publications

References 41 publications

Smart Response Surface Models using Legacy Data for Multidisciplinary Optimization of Aircraft Systems

Smart Response Surface Models using Legacy Data for Multidisciplinary Optimization of Aircraft Systems

Application of Response Surface Methodology to Stiffened Panel Optimization

Parallel surrogate-assisted global optimization with expensive functions – a survey

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