Journal: Structural and Multidisciplinary Optimization

Abbreviation

Struct. Multidiscipl. Optim.

Publisher

Springer

Journal Volumes

ISSN

1615-147X
1615-1488

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Publications 1 - 10 of 19
  • A novel computational framework for structural optimization with patched laminates
    Item type: Journal Article
    Kussmaul, Ralph; Jónasson, Jónas; Zogg, Markus; et al. (2019)
    Structural and Multidisciplinary Optimization
    Fiber patch placement (FPP) is a manufacturing technique for discrete variable stiffness composites. In the FPP approach, a structural component is assembled from a multitude of discrete fiber patches. However, due to the discontinuous fibers at patch edges, complex stress distributions occur. To date, a holistic FPP design framework that combines a tailored patch placement method with a dedicated mechanical model for the analysis of patched laminates does not exist. This article introduces a novel approach for the design of fiber patched laminates. It is based on the sequential placement of patches on a finite element shell mesh, using a critical element and angle selection routine in order to optimally locate and orientate fiber patches. They are added to the 3D mesh by employing a highly efficient kinematic draping algorithm. Strength-critical regions of the resulting fiber patched laminates are identified by state-of-the-art finite element analysis and extracted to a shear-lag–based mechanical submodel dedicated to the detailed analysis of patched laminates. The patch placement routine terminates once all design optimization criteria are met. The efficiency of applying optimized patch reinforcements on a continuous fiber-reinforced base laminate is demonstrated using the example of an individualized biomedical component. The work at hand presents the first patched laminate design framework combining a patch placement strategy coupled with a dedicated mechanical model. As a consequence, a substantial progress in the design of patch laminated structures is achieved.
  • Active expansion sampling for learning feasible domains in an unbounded input space
    Item type: Journal Article
    Chen, Wei; Fuge, Mark (2018)
    Structural and Multidisciplinary Optimization
    Many engineering problems require identifying feasible domains under implicit constraints. One example is finding acceptable car body styling designs based on constraints like aesthetics and functionality. Current active-learning based methods learn feasible domains for bounded input spaces. However, we usually lack prior knowledge about how to set those input variable bounds. Bounds that are too small will fail to cover all feasible domains; while bounds that are too large will waste query budget. To avoid this problem, we introduce Active Expansion Sampling (AES), a method that identifies (possibly disconnected) feasible domains over an unbounded input space. AES progressively expands our knowledge of the input space, and uses successive exploitation and exploration stages to switch between learning the decision boundary and searching for new feasible domains. We show that AES has a misclassification loss guarantee within the explored region, independent of the number of iterations or labeled samples. Thus it can be used for real-time prediction of samples’ feasibility within the explored region. We evaluate AES on three test examples and compare AES with two adaptive sampling methods — the Neighborhood-Voronoi algorithm and the straddle heuristic — that operate over fixed input variable bounds.
  • Global laminate optimization on geometrically partitioned shell structures
    Item type: Journal Article
    Keller, David (2010)
    Structural and Multidisciplinary Optimization
  • Feature-based spline optimization in CAD
    Item type: Journal Article
    Weiss, Daniel (2010)
    Structural and Multidisciplinary Optimization
  • Optimization under worst case constraints - a new global multimodel search procedure
    Item type: Journal Article
    de Paly, Michael; Bürger, Claudius M.; Bayer, Peter (2013)
    Structural and Multidisciplinary Optimization
    A new method is presented that combines heuristic global optimization and multi-model simulation for reliability based risk averse design. The so-called new stack ordering method is motivated from hydrogeology, where high-reliable groundwater management solutions are sought for with a demanding set of equally probable model alternatives. The idea is to only exploit a small subset of these model alternatives or realizations to approximate the objective function to reduce computational costs. The presented automatic procedure dynamically adjusts the subset online during the course of iterative optimization. The test with theoretical reliability based benchmark problems shows that the new method is efficient in regard to optimality and reliability of found solutions already with small subsets of all models. Compared with a previously presented first version of stack ordering, the presented generalized approach proves to be more robust, computationally efficient and of great potential for related problems in reliability based optimization and design. This conclusion is supported by the fact that the new variant requires about one fifth of the objective function evaluations of the older version in order to achieve the same level of reliability. We also show that these findings can be translated to real world problems by bench marking the performance on a well capture problem.
  • Evolutionary truss topology optimization using a graph-based parameterization concept
    Item type: Journal Article
    Giger, M.; Ermanni, P. (2006)
    Structural and Multidisciplinary Optimization
  • Development of CFRP racing motorcycle rims using a heuristic evolutionary algorithm approach
    Item type: Journal Article
    Giger, M.; Ermanni, P. (2005)
    Structural and Multidisciplinary Optimization
  • Topology optimization using PETSc: a Python wrapper and extended functionality
    Item type: Journal Article
    Smit, Thijs; Aage, Niels; Ferguson, Stephen J.; et al. (2021)
    Structural and Multidisciplinary Optimization
    This paper presents a Python wrapper and extended functionality of the parallel topology optimization framework introduced by Aage et al. (Topology optimization using PETSc: an easy-to-use, fully parallel, open source topology optimization framework. Struct Multidiscip Optim 51(3):565-572, 2015). The Python interface, which simplifies the problem definition, is intended to expand the potential user base and to ease the use of large-scale topology optimization for educational purposes. The functionality of the topology optimization framework is extended to include passive domains and local volume constraints among others, which contributes to its usability to real-world design applications. The functionality is demonstrated via the cantilever beam, bracket and torsion ball examples. Several tests are provided which can be used to verify the proper installation and for evaluating the performance of the user's system setup. The open-source code is available at https:// github. com/thsmit/, repository TopOpt_in_PETSc_wrapped_in_Python.
  • A graph-based parameterization concept for global laminate optimization
    Item type: Journal Article
    Giger, M.; Keller, D.; Ermanni, P. (2008)
    Structural and Multidisciplinary Optimization
  • Complex-shaped beam element and graph-based optimization of compliant mechanisms
    Item type: Journal Article
    Sauter, Michael; Kress, Gerald; Giger, Mathias; et al. (2007)
    Structural and Multidisciplinary Optimization
Publications 1 - 10 of 19