Relevant bibliographies by topics / Ak-mcs

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Ak-mcs'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Ak-mcs"

1

Fauriat, W., and N. Gayton. "AK-SYS: An adaptation of the AK-MCS method for system reliability." Reliability Engineering & System Safety 123 (March 2014): 137–44. http://dx.doi.org/10.1016/j.ress.2013.10.010.

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2

Xu, Chunlong, Weidong Chen, Jingxin Ma, Yaqin Shi, and Shengzhuo Lu. "AK-MSS: An adaptation of the AK-MCS method for small failure probabilities." Structural Safety 86 (September 2020): 101971. http://dx.doi.org/10.1016/j.strusafe.2020.101971.

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Yun, Wanying, Zhenzhou Lu, Xian Jiang, Leigang Zhang, and Pengfei He. "AK-ARBIS: An improved AK-MCS based on the adaptive radial-based importance sampling for small failure probability." Structural Safety 82 (January 2020): 101891. http://dx.doi.org/10.1016/j.strusafe.2019.101891.

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4

Ling, Chunyan, Zhenzhou Lu, and Xiaobo Zhang. "An efficient method based on AK-MCS for estimating failure probability function." Reliability Engineering & System Safety 201 (September 2020): 106975. http://dx.doi.org/10.1016/j.ress.2020.106975.

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5

Xiong, Yifang, and Suresh Sampath. "A fast-convergence algorithm for reliability analysis based on the AK-MCS." Reliability Engineering & System Safety 213 (September 2021): 107693. http://dx.doi.org/10.1016/j.ress.2021.107693.

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Razaaly, Nassim, and Pietro Marco Congedo. "Extension of AK-MCS for the efficient computation of very small failure probabilities." Reliability Engineering & System Safety 203 (November 2020): 107084. http://dx.doi.org/10.1016/j.ress.2020.107084.

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7

Echard, B., N. Gayton, and M. Lemaire. "AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation." Structural Safety 33, no. 2 (March 2011): 145–54. http://dx.doi.org/10.1016/j.strusafe.2011.01.002.

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8

Wei, Pengfei, Chenghu Tang, and Yuting Yang. "Structural reliability and reliability sensitivity analysis of extremely rare failure events by combining sampling and surrogate model methods." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233, no. 6 (May 17, 2019): 943–57. http://dx.doi.org/10.1177/1748006x19844666.

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The aim of this article is to study the reliability analysis, parametric reliability sensitivity analysis and global reliability sensitivity analysis of structures with extremely rare failure events. First, the global reliability sensitivity indices are restudied, and we show that the total effect index can also be interpreted as the effect of randomly copying each individual input variable on the failure surface. Second, a new method, denoted as Active learning Kriging Markov Chain Monte Carlo (AK-MCMC), is developed for adaptively approximating the failure surface with active learning Kriging surrogate model as well as dynamically updated Monte Carlo or Markov chain Monte Carlo populations. Third, the AK-MCMC procedure combined with the quasi-optimal importance sampling procedure is extended for estimating the failure probability and the parametric reliability sensitivity and global reliability sensitivity indices. For estimating the global reliability sensitivity indices, two new importance sampling estimators are derived. The AK-MCMC procedure can be regarded as a combination of the classical Monte Carlo Simulation (AK-MCS) and subset simulation procedures, but it is much more effective when applied to extremely rare failure events. Results of test examples show that the proposed method can accurately and robustly estimate the extremely small failure probability (e.g. 1e–9) as well as the related parametric reliability sensitivity and global reliability sensitivity indices with several dozens of function calls.
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Feng, Kaixuan, Zhenzhou Lu, Chunyan Ling, and Wanying Yun. "Efficient computational method based on AK-MCS and Bayes formula for time-dependent failure probability function." Structural and Multidisciplinary Optimization 60, no. 4 (April 15, 2019): 1373–88. http://dx.doi.org/10.1007/s00158-019-02265-z.

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10

Peijuan, Zheng, Wang Chien Ming, Zong Zhouhong, and Wang Liqi. "A new active learning method based on the learning function U of the AK-MCS reliability analysis method." Engineering Structures 148 (October 2017): 185–94. http://dx.doi.org/10.1016/j.engstruct.2017.06.038.

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Dissertations / Theses on the topic "Ak-mcs"

1

Boniface, Benjamin. "Modélisation thermo-mécanique et fiabilité des échangeurs réacteurs en carbure de silicium." Thesis, Toulouse, INPT, 2010. http://www.theses.fr/2010INPT0050/document.

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Les échangeurs réacteurs intensifiés (ERI) sont des échangeurs de chaleur utilisés comme réacteurs chimiques afin d'intensifier les procédés de synthèse. L'ERI étudié dans ce travail est composé de plaques en carbure de silicium, lui conférant ainsi des propriétés thermiques et chimiques importantes. En contrepartie, l'utilisation du SiC accentue les risques de rupture fragile, à ajouter aux risques principaux associés aux ERI qui sont les fuites de fluide et l'emballement thermique de la réaction. L'intensification des procédés représente un gain important en terme de productivité, de sécurité et d'impact environnemental, à condition de maîtriser la conception et la mise en œuvre de l'ERI. L'objectif de ce travail est de proposer une démarche d'étude adaptée à ce type de produit en général, et d'optimiser la conception de cet ERI en particulier. Pour cela, les modèles thermo-chimique et thermo-mécanique de l'ERI sont développés puis utilisés dans une étude mécano-fiabiliste, notamment au moyen à la méthode AK-MCS basée sur l'interpolation par krigeage de la fonction de performance
Intensified heat exchanger reactors (ERI) are heat exchangers used as chemical reactors for process intensification. The ERI studied in this work is composed of silicon carbide (SiC) based plates, giving it interesting thermal and chemical properties. In return, the use of SiC increases the brittle fracture risk, in addition of the main risks that are leaking fluid and thermal runaway reaction. The process intensification represents a significant gain in productivity, safety and environmental impact, provided that the design and the implementation procedure of the ERI are optimized. The objective of this work is to propose a process of study suited to this type of product in general, and to optimize the design of this ERI in particular. For this, the thermo-chemical and thermo-mechanical models are developed and used in a mechano-reliability study, notably using the AK-MCS method based on kriging interpolation of the performance function
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Book chapters on the topic "Ak-mcs"

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"Structural reliability assessment using Kriging metamodel and Monte Carlo simulation: AK-MCS method." In Reliability and Optimization of Structural Systems, 73–80. CRC Press, 2010. http://dx.doi.org/10.1201/b10497-9.

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Conference papers on the topic "Ak-mcs"

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Morgane, Menz, Sylvain Dubreuil, Jerome Morio, Nathalie Bartoli, and Christian Gogu. "Variance Analysis of AK-MCS Failure Probability Estimator and Algorithmic Application." In Proceedings of the 29th European Safety and Reliability Conference (ESREL). Singapore: Research Publishing Services, 2020. http://dx.doi.org/10.3850/978-981-14-8593-0_3998-cd.

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2

Wang, Zining, Zhenguo Zhang, Huanyang Zhao, Xianbing Chen, Shenghui Yuan, and Shacheng Jin. "Reliability Analysis of Thermal Warping Deformation of Brake Disc Based on AK-MCS Method." In 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE). IEEE, 2020. http://dx.doi.org/10.1109/icmcce51767.2020.00103.

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