A single-loop method for reliability-based design optimisation

Liang, Jing; Mourelatos, Zissimos P.; Tu, Jian

doi:10.1504/ijpd.2008.016371

Cited by 126 publications

(47 citation statements)

References 18 publications

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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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“…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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“…The diversity of system structures, resource constraints and types of uncertainty considered has led to the construction and analysis of various design optimization models (or design methods under uncertainty) (Liang et al, 2008;Huang et al, 2012a;2012b;Huang et al, 2011b;Huang and Zhang, 2009;Huang et al, 2008):…”

Section: (2) Classifications Of Design Optimizationmentioning

confidence: 99%

Possibility and Evidence-Based Reliability Analysis and Design Optimization

Huang

Liu

et al. 2013

American Journal of Engineering and Applied Sciences

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Engineering design under uncertainty has gained considerable attention in recent years. A great multitude of new design optimization methodologies and reliability analysis approaches are put forth with the aim of accommodating various uncertainties. Uncertainties in practical engineering applications are commonly classified into two categories, i.e., aleatory uncertainty and epistemic uncertainty. Aleatory uncertainty arises because of unpredictable variation in the performance and processes of systems, it is irreducible even adding more data or knowledge. On the other hand, epistemic uncertainty stems from lack of knowledge of the system due to limited data, measurement limitations, or simplified approximations in modeling system behavior and it can be reduced by obtaining more data or knowledge. More specifically, aleatory uncertainty is naturally represented by a statistical distribution and its associated parameters can be characterized by sufficient data. If, however, the data is limited and can be quantified in a statistical sense, epistemic uncertainty can be considered as an alternative tool in such a situation. Of the several optional treatments for epistemic uncertainty, possibility theory and evidence theory have proved to be the most computationally efficient and stable for reliability analysis and engineering design optimization. This study first attempts to provide a better understanding of uncertainty in engineering design by giving a comprehensive overview of its classifications, theories and design considerations. Then a review is conducted of general topics such as the foundations and applications of possibility theory and evidence theory. This overview includes the most recent results from theoretical research, computational developments and performance improvement of possibility theory and evidence theory with an emphasis on revealing the capability and characteristics of quantifying uncertainty from different perspectives. Possibility and evidence theory-based reliability methods have many advantages for practical engineering when compared with traditional probability-based reliability methods. They can work well under limited data while the latter need large amounts of information, more than possible in engineering practice due to aleatory and epistemic uncertainties. The possible directions for future work are summarized.

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“…There are several approaches in the literature to address the coupled reliability-based optimization problems, e.g. 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.…”

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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A single-loop method for reliability-based design optimisation

Cited by 126 publications

References 18 publications

Probabilistic constraint reasoning

Probabilistic constraint reasoning

Possibility and Evidence-Based Reliability Analysis and Design Optimization

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

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