Reliability based structural design optimization for practical applications

Chen, Xiaoguang; Hasselman, T. K.; Neill, D. J.

doi:10.2514/6.1997-1403

Cited by 256 publications

(114 citation statements)

References 3 publications

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“…During each cycle, a deterministic optimization problem is solved, followed by reliability assessment and a convergence check. Chen et al 21 also developed a sequential RBDO methodology that was recently generalized for non-normal distributions by Wang and Kodiyalam 22 and extended for multidisciplinary systems by Agarwal et al 23 In this methodology, the lower-level optimization is eliminated, and the MPP of failure corresponding to the probabilistic constraints is estimated implicitly by using a nonlinear transformation based on the direction cosines of the hard constraints at the mean values of the random variables. This methodology is shown to be extremely efficient.…”

Section: B Sequential Methods For Rbdomentioning

confidence: 99%

Reliability-based design using variable-fidelity optimization

Gano

Renaud

Agarwal

et al. 2006

Structure and Infrastructure Engineering

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Competitive marketplaces have driven the need for simulation based design optimization to produce efficient and cost effective designs. However, such design practices are typically deterministic and don't take into account model uncertainties or manufacturing tolerances. Deterministic designs may lie on failure driven constraints, resulting in designs characterized by a high probability of failure. Reliability based design optimization (RBDO) methods have been developed to obtain designs that optimize a merit function while ensuring a target reliability level. Unfortunately, these methods are notorious for the high computational expense they require to converge. There has been a considerable effort to reduce the computational cost, many of these methods have used approximations which are not guaranteed to converge to the optimal solution of the original high fidelity problem. In this research variable fidelity methods are used to reduce the cost of RBDO. Variable fidelity methods use a set of models with varying degrees of fidelity and computational expense to reduce the cost of optimization. The variable fidelity RBDO methodology developed in this investigation is demonstrated on two test cases: a nonlinear analytic problem and a high-lift airfoil design problem. For each of these problems the proposed method shows considerable savings for performing RBDO as compared with standard approaches.

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Section: B Sequential Methods For Rbdomentioning

confidence: 99%

Reliability-based design using variable-fidelity optimization

Gano

Renaud

Agarwal

et al. 2006

Structure and Infrastructure Engineering

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“…Other methods convert the problem into single-loop deterministic optimization [5,31,82]. For an overview of such methods see [38].…”

Section: Classical Techniquesmentioning

confidence: 99%

Probabilistic constraint reasoning

Carvalho¹

2015

Constraints

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perpétuo e sem limites geográficos, de arquivar e publicar esta dissertação através de exemplares impressos reproduzidos em papel ou de forma digital, ou por qualquer outro meio conhecido ou que venha a ser inventado, e de a divulgar através de repositórios científicos e de admitir a sua cópia e distribuição com objectivos educacionais ou de investigação, não comerciais, desde que seja dado crédito ao autor e editor.For Laura with love.My endless source of inspiration with her tender heart and ingenious mind. AcknowledgementsFor me, writing this thesis was like a long, long journey, with many ups and downs. In every step of the way I faced challenges that made me grow as a researcher and as a person. But the journey is coming to an end and is now time to remember and be grateful to those who crossed my path and helped me to achieve my goal in many different ways. Neste trabalho desenvolvemos um paradigma de restrições em domínios contínuos que associa um espaço probabilísticoàs variáveis do problema, permitindo efectuar raciocínio probabilístico. Tal raciocínio baseia-se na avaliação de informação probabilística que requer a computação de integrais multidimensionais em regiões possivelmente não lineares. São desenvolvidos algoritmos capazes de avaliar essa informação, usando técnicas de integração seguras ou aproximadas e dependendo de métodos de programação por restrições em domínios contínuos para obter coberturas da região de integração.A plataforma probabilística de restrições em domínios contínuosé adequada para suporteà decisão em problemas não lineares em domínios contínuos, com incerteza. A sua aplicabilidadeé ilustrada em problemas inversos e problemas de fiabilidade, que são duas classes distintas de problemas de engenharia, representativas do tipo de raciocínio com incerteza requerido pelos decisores.

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“…single level approaches (SLA) that either merge the inner reliability analysis loop and outer optimization loop into one single level problem (Chen et al 1997;Agarwal et al 2004;Liang and Mourelatos 2008) or decouple the double loop into sequential cycles of reliability analysis and deterministic MDO (Royset et al 2001;Wu et al 2001;Du and Chen 2002), which are surveyed in (Valdebenito and Schuëller 2010). Herein the SLA method proposed in (Chen et al 1997) is used for simplicity. In optimization, the reliability constraint Pr{g(Q) ≤ 0} ≥ R g is reformulated as…”

Section: Uncertainty Analysismentioning

confidence: 99%

A surrogate based multistage-multilevel optimization procedure for multidisciplinary design optimization

Yao

Chen

Ouyang

et al. 2011

Struct Multidisc Optim

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Optimization procedure is one of the key techniques to address the computational and organizational complexities of multidisciplinary design optimization (MDO). Motivated by the idea of synthetically exploiting the advantage of multiple existing optimization procedures and meanwhile complying with the general process of satellite system design optimization in conceptual design phase, a multistage-multilevel MDO procedure is proposed in this paper by integrating multiple-discipline-feasible (MDF) and concurrent subspace optimization (CSSO), termed as MDF-CSSO. In the first stage, the approximation surrogates of high-fidelity disciplinary models are built by disciplinary specialists independently, based on which the single level optimization procedure MDF is used to quickly identify the promising region and roughly locate the optimum of the MDO problem. In the second stage, the disciplinary specialists are employed to further investigate and improve the baseline design obtained in the first stage with highfidelity disciplinary models. CSSO is used to organize the concurrent disciplinary optimization and system coordination so as to allow disciplinary autonomy. To enhance the reliability and robustness of the design under uncertainties, the probabilistic version of MDF-CSSO (PMDF-CSSO) is developed to solve uncertainty-based optimization problems. The effectiveness of the proposed methods is verified with one MDO benchmark test and one practical satellite conceptual design optimization problem, followed by conclusion remarks and future research prospects.

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Reliability based structural design optimization for practical applications

Cited by 256 publications

References 3 publications

Reliability-based design using variable-fidelity optimization

Reliability-based design using variable-fidelity optimization

Probabilistic constraint reasoning

A surrogate based multistage-multilevel optimization procedure for multidisciplinary design optimization

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