Neumann expansion for fuzzy finite element analysis

Lallemand, Bertrand; Plessis, G.; Tison, Thierry; Level, P.

doi:10.1108/02644409910277933

Cited by 25 publications

(9 citation statements)

References 30 publications

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“…The Neumann expansion [18], the transformation method [19], and response surface based methods [20] are proposed for fuzzy analysis. In this context, recently fuzzy analysis is employed to deal with uncertainties in engineering problems using only available data [21].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Bottom up surrogate based approach for stochastic frequency response analysis of laminated composite plates

Dey

Mukhopadhyay

Spickenheuer

et al. 2016

Composite Structures

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  Fuzzy uncertainty propagation in free vibration of composite plate is analyzed.  Gram-Schmidt polynomial chaos expansion is employed as surrogate.  Design points are selected using D-optimal design algorithm.  Fuzzy mode shapes and frequency response functions are presented.

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Section: Accepted Manuscriptmentioning

confidence: 99%

Bottom up surrogate based approach for stochastic frequency response analysis of laminated composite plates

Dey

Mukhopadhyay

Spickenheuer

et al. 2016

Composite Structures

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“…Methods applicable for interval analysis, such as classical interval arithmetic [31], affine analysis [32,33] or vertex theorems [34] can be used. The Neumann expansion [35], the transformation method [30,36,37] and more recently, response surface based methods [34,38,39] have also been proposed for fuzzy uncertainty propagation. Among the existing methods, optimization methods are found to be the most accurate one in fuzzy propagation analysis [26,40].…”

Section: Fuzzy Analysismentioning

confidence: 99%

Flutter Uncertainty Quantification for the S4T Model

Tartaruga¹,

Cooper²,

Γεωργίου

et al. 2017

55th AIAA Aerospace Sciences Meeting

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms The application of uncertainty analysis, including probabilistic and non-probabilistic approaches, to the prediction of the dynamic pressure at which flutter occurs is considered for the S4T Semi-Span Super-Sonic Transport wind-tunnel model. A SVD based reduced order modelling approach, along with the Polynomial Chaos Expansion and Fuzzy Analysis methods for uncertainty quantification, are described and used to explore the effects that variations in the structural mass, resembling variations in the fuel load, have on the linear flutter analyses for a range of subsonic and supersonic speeds. All the approaches are able to efficiently quantify the effects of structural uncertainty on the aeroelastic behavior.

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“…This is achieved by finding the coefficients of the differential equation that governs the relationship between the independent and dependent variables [21]. Methods like Neumann expansion [22] and perturbation method [23] can help when extracting these coefficients through the approximation of differential equation. However, it can never be guaranteed that the often complex and nonlinear relationship that exists between system variables can be approximated with a sufficiently low error margin using differential equations alone [7].…”

Section: A Differential Analysismentioning

confidence: 99%

Event Tracking for Real-Time Unaware Sensitivity Analysis (EventTracker)

Tavakoli

Mousavi

Broomhead

2013

IEEE Trans. Knowl. Data Eng.

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Abstract-This paper introduces a platform for online Sensitivity Analysis (SA) that is applicable in large scale real-time data acquisition (DAQ) systems. Here we use the term real-time in the context of a system that has to respond to externally generated input stimuli within a finite and specified period. Complex industrial systems such as manufacturing, healthcare, transport, and finance require high quality information on which to base timely responses to events occurring in their volatile environments. The motivation for the proposed EventTracker platform is the assumption that modern industrial systems are able to capture data in real-time and have the necessary technological flexibility to adjust to changing system requirements. The flexibility to adapt can only be assured if data is succinctly interpreted and translated into corrective actions in a timely manner. An important factor that facilitates data interpretation and information modelling is an appreciation of the affect system inputs have on each output at the time of occurrence. Many existing sensitivity analysis methods appear to hamper efficient and timely analysis due to a reliance on historical data, or sluggishness in providing a timely solution that would be of use in real-time applications. This inefficiency is further compounded by computational limitations and the complexity of some existing models.In dealing with real-time event driven systems, the underpinning logic of the proposed method is based on the assumption that in the vast majority of cases changes in input variables will trigger events. Every single or combination of events could subsequently result in a change to the system state. The proposed event tracking sensitivity analysis method describes variables and the system state as a collection of events. The higher the numeric occurrence of an input variable at the trigger level during an event monitoring interval, the greater is its impact on the final analysis of the system state.Experiments were designed to compare the proposed event tracking sensitivity analysis method with a comparable method (that of Entropy). An improvement of 10% in computational efficiency without loss in accuracy was observed. The comparison also showed that the time taken to perform the sensitivity analysis was 0.5% of that required when using the comparable Entropy based method. ATA acquisition systems that deal with large quantities of input variables and have higher sampling frequencies result in high bandwidth communication and place a heavy computational load on the higher tier data processing and information systems within their hierarchy. The focus of researchers and practitioners in this area has been to minimize this computational overhead by eliminating input variables that have the least impact on the system, this so called sensitivity analysis is discussed in [1]- [6]. Sensitivity analysis techniques help system analysts to focus on the most valuable information, information that most significantly impacts on system behaviour. Sensitivit...

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Neumann expansion for fuzzy finite element analysis

Cited by 25 publications

References 30 publications

Bottom up surrogate based approach for stochastic frequency response analysis of laminated composite plates

Bottom up surrogate based approach for stochastic frequency response analysis of laminated composite plates

Flutter Uncertainty Quantification for the S4T Model

Event Tracking for Real-Time Unaware Sensitivity Analysis (EventTracker)

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