A surrogate modeling approach for reliability analysis of a multidisciplinary system with spatio-temporal output

Hu, Zhen; Mahadevan, Sankaran

doi:10.1007/s00158-017-1737-x

Cited by 22 publications

(8 citation statements)

References 52 publications

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“…If the surrogate models need to be refined, the input setting at which the surrogate models need to be refined is identified by (25), the questions of whether, where, and when (summarized in Sec. 2.4) have been answered.…”

Section: Error Analysis Of the Multidisciplinary Systemmentioning

confidence: 99%

“…Such a method has been presented in Ref. [25]. Based on the dimension reduction methods, the proposed adaptive surrogate modeling framework can be employed to reduce the required computational effort for surrogate modeling to achieve an accurate prediction of the system failure probability.…”

Section: A Compound Cylindermentioning

confidence: 99%

“…A detailed description of this panel structure is available in Refs. [23] and [25]. Figure 2 shows the four disciplinary analysis models of the panel (aerodynamics, aerodynamic heating, heat transfer, and structural deformation) [23].…”

Section: Introductionmentioning

confidence: 99%

“…The developed method in this paper could be integrated with the method presented in Ref. [25] to handle spatial and temporal variability simultaneously. In what follows, we first provide a generalized model of multidisciplinary analysis under time-dependent uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Surrogate Modeling for Time-Dependent Multidisciplinary Reliability Analysis

Mahadevan

2017

Journal of Mechanical Design

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Multidisciplinary systems with transient behavior under time-varying inputs and coupling variables pose significant computational challenges in reliability analysis. Surrogate models of individual disciplinary analyses could be used to mitigate the computational effort; however, the accuracy of the surrogate models is of concern, since the errors introduced by the surrogate models accumulate at each time-step of the simulation. This paper develops a framework for adaptive surrogate-based multidisciplinary analysis (MDA) of reliability over time (A-SMART). The proposed framework consists of three modules, namely, initialization, uncertainty propagation, and three-level global sensitivity analysis (GSA). The first two modules check the quality of the surrogate models and determine when and where we should refine the surrogate models from the reliability analysis perspective. Approaches are proposed to estimate the potential error of the failure probability estimate and to determine the locations of new training points. The three-level GSA method identifies the individual surrogate model for refinement. The combination of the three modules facilitates adaptive and efficient allocation of computational resources, and enables high accuracy in the reliability analysis result. The proposed framework is illustrated with two numerical examples.

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Section: Error Analysis Of the Multidisciplinary Systemmentioning

confidence: 99%

Section: A Compound Cylindermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Surrogate Modeling for Time-Dependent Multidisciplinary Reliability Analysis

Mahadevan

2017

Journal of Mechanical Design

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“…Thus, the solving efficiency of MDSA could be essential to enhance the MDO process on coupled systems. Furthermore, different multidisciplinary analysis and optimization strategies under uncertainty are developed to handle the stochastic and/or epistemic uncertainties in coupled engineering problems [8][9][10]. A likelihood-based approach is proposed to estimate the probability density function of coupling variables [11] and is further extended to handle the model uncertainty [12] and the uncertainty propagation in high dimensional coupled systems [13].…”

Section: Introductionmentioning

confidence: 99%

A Method for Multidisciplinary System Analysis Based on Minimal Feedback Variables

Yin

Wang

2017

Mathematical Problems in Engineering

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As modern engineering design usually involves dependence of one discipline on another, multidisciplinary system analysis (MDSA) plays an important role in the multidisciplinary simulation and design optimization on coupled systems. The paper proposes an MDSA method based on minimal feedback variables (MDSA MF) to enhance the solving efficiency. There are two phases in the method. In phase 1, design structural matrix (DSM) is introduced to represent a coupled system, and each off-diagonal element is denoted by a coupling variable set; then an optimal sequence model is built to obtain a reordered DSM with minimal number of feedback variables. In phase 2, the feedback in the reordered DSM is broken, so that the coupled system is transformed into one directed acyclic graph; then, regarding the inputs depending on the broken feedback as independent variables, a least-squares problem is constructed to minimize the residuals of these independents and corresponding outputs to zero, which means the multidisciplinary consistence is achieved. Besides, the MDSA MF method is implemented in a multidisciplinary platform called FlowComputer. Several examples of coupled systems are modeled and solved in the platform using several MDSA methods. The results demonstrate that the proposed method could enhance the solving efficiency of coupled systems.

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A nested single-loop Kriging model-based method for time-dependent failure credibility

Wei

Lü

Feng

et al. 2020

Struct Multidisc Optim

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A surrogate modeling approach for reliability analysis of a multidisciplinary system with spatio-temporal output

Cited by 22 publications

References 52 publications

Adaptive Surrogate Modeling for Time-Dependent Multidisciplinary Reliability Analysis

Adaptive Surrogate Modeling for Time-Dependent Multidisciplinary Reliability Analysis

A Method for Multidisciplinary System Analysis Based on Minimal Feedback Variables

A nested single-loop Kriging model-based method for time-dependent failure credibility

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