Interval prediction of responses for uncertain multidisciplinary system

Wang, Xiaojun; Wang, Ruixing; Chen, Xianjia; Geng, Xinyu; Fan, Weichao

doi:10.1007/s00158-016-1601-4

Cited by 16 publications

(3 citation statements)

References 34 publications

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“…In recent years, several methods of multidisciplinary interval uncertainty analysis have been developed, namely, interval vertex theorem, 19 first-order interval Taylor expansion method, 43 and optimization approach. 44 The interval vertex theorem requires a premise that the output function of discipline is monotonic, which is not always satisfied in complex problems. 45 The interval Taylor expansion method is an approximation method with high efficiency but is more suitable for the linear and nearly linear problems.…”

Section: Multidisciplinary Interval Uncertainty Analysismentioning

confidence: 99%

“…Therefore, in the DRA method, the object of multidisciplinary interval uncertainty analysis is to solve the means and deviations of coupling variables. In recent years, several methods of multidisciplinary interval uncertainty analysis have been developed, namely, interval vertex theorem, 19 first‐order interval Taylor expansion method, 43 and optimization approach 44 . The interval vertex theorem requires a premise that the output function of discipline is monotonic, which is not always satisfied in complex problems 45 .…”

Section: Algorithm Of Multidisciplinary Nonprobabilistic Reliability ...mentioning

confidence: 99%

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Interval uncertainty‐based multidisciplinary reliability analysis method for information‐poor complex system

Fang

Tang

Zhang

et al. 2022

Numerical Meth Engineering

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Due to integrated structures and multiple functions, complex systems, such as large-scale equipment and aerospace vehicle, faced prominent reliability problems. However, in real-world applications, collecting sufficient reliability data is costly and time-consuming. To overcome this difficulty, the information-poor variables are modeled with interval models and the corresponding reliability analysis method for complex system is studied in this paper. First, we establish a multidisciplinary nonprobabilistic reliability model based on interval analysis, which is an optimization framework with an objective of nonprobabilistic reliability index and two constraints of interdisciplinary consistency equation and limit state equation. Second, two types of algorithms for the above model are studied. Based on typical methods of multidisciplinary design optimization (MDO), the direct algorithms including RA-MDF and RA-IDF are developed.Then, a decoupled reliability analysis algorithm is proposed to realize parallel multidisciplinary reliability analysis, in which two methods of multidisciplinary interval uncertainty analysis are formulated to decouple the coupling relationship between disciplines. Finally, three examples are employed to illustrate the validity and efficiency of the proposed methods.

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Section: Multidisciplinary Interval Uncertainty Analysismentioning

confidence: 99%

Section: Algorithm Of Multidisciplinary Nonprobabilistic Reliability ...mentioning

confidence: 99%

Interval uncertainty‐based multidisciplinary reliability analysis method for information‐poor complex system

Fang

Tang

Zhang

et al. 2022

Numerical Meth Engineering

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“…, k j q is the jth root of the (n + 1)th Chebyshev polynomial. Several methods are available to calculate the lower and upper bounds of Chebyshev polynomials in equation (20), such as the optimization method 25 and the interval arithmetic. 22 It should be mentioned that the optimization method is usually quite time-consuming, and the interval arithmetic could lead to overestimation and be conservative.…”

Section: The Chebyshev Polynomialmentioning

confidence: 99%

An efficient analysis and optimization method for powertrain mounting systems involving interval uncertainty

Cai

Shangguan

Lü

2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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Uncertainty widely exists in the powertrain mounting system of a vehicle. The traditional analysis and optimization methods for powertrain mounting system design are often based on deterministic or random models. In this study, an efficient analysis and optimization method is developed for powertrain mounting system design involving interval uncertainty. In the proposed method, the uncertain parameters of powertrain mounting system are treated as interval variables, and an efficient method called as Chebyshev-Vertex method is developed to fast calculate the lower and upper bounds of the natural frequencies and decoupling ratios of powertrain mounting system. Monte-Carlo method is taken as a reference method to verify the calculation. Then, an optimization model is established based on Chebyshev-Vertex method, in which the interval responses of decoupling ratios are used to build up optimization objective while the interval responses of natural frequencies and decoupling ratios are taken to create optimization constraints. The optimization model of the powertrain mounting system with interval parameters is generally a double-loop nested problem, and it is rather time-consuming on calculation. However, based on Chebyshev-Vertex method, the optimization model can be approximately simplified into a single-loop one and the calculation efficiency is greatly improved. A numerical example is provided to demonstrate the effectiveness of the proposed method on the analysis and optimization design of the powertrain mounting system involving interval uncertainty.

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Improved collaboration pursuing method for multidisciplinary robust design optimization

Xiao

Gao

2018

Struct Multidisc Optim

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Interval prediction of responses for uncertain multidisciplinary system

Cited by 16 publications

References 34 publications

Interval uncertainty‐based multidisciplinary reliability analysis method for information‐poor complex system

Interval uncertainty‐based multidisciplinary reliability analysis method for information‐poor complex system

An efficient analysis and optimization method for powertrain mounting systems involving interval uncertainty

Improved collaboration pursuing method for multidisciplinary robust design optimization

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