Stochastic free vibration analysis of angle-ply composite plates – A RS-HDMR approach

Dey, Sudip; Mukhopadhyay, T.; Adhikari, Sondipon

doi:10.1016/j.compstruct.2014.09.057

Cited by 74 publications

(35 citation statements)

References 28 publications

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“…The chronological analysis of comparative studies of metamodeling techniques summarised in Table 1 illustrates well this trend, showing the evolution from polynomial based response surface methods introduced by Box and Draper (1987), to the prevalence of Kriging and RBF, and more recently the increasing use of techniques like SVR Pan et al 2010;Wang et al 2011). Also, the emerging interest in tackling highdimensional engineering problems led to increased use of HDMR techniques (Huang et al 2015;Dey et al 2015;Liu et al 2016;Chen et al 2018).…”

Section: Review Of Related Workmentioning

confidence: 80%

Performance evaluation of metamodelling methods for engineering problems: towards a practitioner guide

Kianifar

Campean

2019

Struct Multidisc Optim

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Metamodelling or surrogate modelling techniques are frequently used across the engineering disciplines in conjunction with expensive simulation models or physical experiments. With the proliferation of metamodeling techniques developed to provide enhanced performance for specific problems, and the wide availability of a diverse choice of tools in engineering software packages, the engineering task of selecting a robust metamodeling technique for practical problems is still a challenge. This research introduces a framework for describing the typology of engineering problems, in terms of dimensionality and complexity, and the modelling conditions, reflecting the noisiness of the signals and the affordability of sample sizes, and on this basis presents a systematic evaluation of the performance of frequently used metamodeling techniques. A set of metamodeling techniques, selected based on their reported use for engineering problems (i.e. Polynomial, Radial Basis Function, and Kriging), were systematically evaluated in terms of accuracy and robustness against a carefully assembled set of 18 test functions covering different types of problems, sampling conditions and noise conditions. A set of four real-world engineering case studies covering both computer simulation and physical experiments were also analysed as validation tests for the proposed guidelines. The main conclusions drawn from the study are that Kriging model with Matérn 5/2 correlation function performs consistently well across different problem types with smooth (i.e. not noisy) data, while Kriging model with Matérn 3/2 correlation function provides robust performance under noisy conditions, except for the very high noise conditions, where the Kriging model with nugget appears to provide better models. These results provide engineering practitioners with a guide for the choice of a metamodeling technique for problem types and modelling conditions represented in the study, whereas the evaluation framework and benchmarking problems set will be useful for researchers conducting similar studies. Structural and Multidisciplinary Optimization (2020) 61:159-186 Performance evaluation of metamodelling methods for engineering problems: towards a practitioner guide 161

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Section: Review Of Related Workmentioning

confidence: 80%

Performance evaluation of metamodelling methods for engineering problems: towards a practitioner guide

Kianifar

Campean

2019

Struct Multidisc Optim

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“…A surrogate model is employed by using the generalized high dimensional model representation (GHDMR) approach wherein D-MORPH (Diffeomorphic Modulation under Observable Response Preserving Homotopy) regression is employed to ensure the hierarchical orthogonality of HDMR component functions [16]. Random sampling high dimensional model representation (RS-HDMR) was employed for uncertainty quantification of natural frequency in composite plates considering three input parameters namely fibre-orientation angle, elastic modulus and mass density [17], wherein the input parameters are independent to each other. In contrast, investigation is also carried out with a new element for laminated composite plates [18] while stochastic modeling of unidirectional composites is studied considering delamination [19].…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

Thermal uncertainty quantification in frequency responses of laminated composite plates

Dey

Mukhopadhyay

Sahu

et al. 2015

Composites Part B: Engineering

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The propagation of thermal uncertainty in composite structures has significant computational challenges. This paper presents the thermal, ply-level and material uncertainty propagation in frequency responses of laminated composite plates by employing surrogate model which is capable of dealing with both correlated and uncorrelated input parameters. The present approach introduces the generalized high dimensional model representation (GHDMR) wherein diffeomorphic modulation under observable response preserving homotopy (D-MORPH) regression is utilized to ensure the hierarchical orthogonality of high dimensional model representation component functions. The stochastic range of thermal field includes elevated temperatures up to 375K and sub-zero temperatures up to cryogenic range of 125K.Statistical analysis of the first three natural frequencies is presented to illustrate the results and its performance.

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“…While forming CCD models, insignificant input features are screened out and not considered in the final meta-model. A quantitative evaluation for effect of each parameter on the total model variance is carried out using analysis of variance (ANOVA) method according to its F-test value (28) where …”

Section: Surrogate Model -Central Composite Designmentioning

confidence: 99%

“…A stochastic multi-scale approach on composite is employed by using elastodynamic wave equation [25] while a statistical modelling for uncertainty quantification is used in frequency response function estimation [26]. The uncertainty quantification in dynamic responses of composite plates is investigated by Sepahvand et al [27] and Dey et al [28] while Jagtap et al [29] studied the stochastic nonlinear free vibration analysis of elastically supported functionally graded materials plate with system randomness in thermal environment. The design of composite laminates is studied by a Monte Carlo method [30] while stochastic finite element method is employed for combined random material and geometric properties of composite shells [31].…”

Section: Introductionmentioning

confidence: 99%

Rotational and ply-level uncertainty in response of composite shallow conical shells

Dey

Mukhopadhyay

Khodaparast

et al. 2015

Composite Structures

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Rotational and ply-level uncertainty in response of composite shallow conical shells, Composite Structures (2015), doi: http://dx. AbstractThis paper presents the quantification of rotational and ply level uncertainty of random natural frequency for laminated composite conical shells by using surrogate modelling approach. The stochastic eigenvalue problem is solved by using QR iteration algorithm.Sensitivity analysis is carried out to address the influence of different input parameters on the output natural frequencies. The sampling size and computational cost is reduced by employing the present approach compared to direct Monte Carlo simulation. The stochastic mode shapes are also depicted for a typical laminate configuration. Statistical analysis is presented to illustrate the results and its performance.

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Stochastic free vibration analysis of angle-ply composite plates – A RS-HDMR approach

Cited by 74 publications

References 28 publications

Performance evaluation of metamodelling methods for engineering problems: towards a practitioner guide

Performance evaluation of metamodelling methods for engineering problems: towards a practitioner guide

Thermal uncertainty quantification in frequency responses of laminated composite plates

Rotational and ply-level uncertainty in response of composite shallow conical shells

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