Stochastic dynamic behaviour of hydrodynamic journal bearings including the effect of surface roughness

Maharshi, K.; Mukhopadhyay, T.; Roy, Biswajit; Roy, Lintu; Dey, Sudip

doi:10.1016/j.ijmecsci.2018.04.012

Cited by 52 publications

(22 citation statements)

References 74 publications

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“…In case of such complex input-output relationships, an efficient surrogate based Monte Carlo simulation approach can be adopted to carry out the search for minimum and maximum values of the output quantities of interest for a particular α-cut level. In this analysis, radial basis function [Beatson (1999), Fasshauer (1997, Hon and Mao (2001), Kansa (1990aKansa ( , 1990b, Kansa and Hon (2000)] is employed as a surrogate , Karsh et al (2018b), Maharshi et al (2018), Metya et al (2017), Mahata et al (2016), Dey et al (2015Dey et al ( , 2016dDey et al ( , 2016e, 2018b] of the actual finite element model of composites.…”

Section: Theoretical Formulation For Fuzzy Finite Element Analysis Of Compositesmentioning

confidence: 99%

Spatially varying fuzzy multi-scale uncertainty propagation in unidirectional fibre reinforced composites

Naskar

Mukhopadhyay

Sriramula

2019

Composite Structures

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This article presents a non-probabilistic fuzzy based multi-scale uncertainty propagation framework for studying the dynamic and stability characteristics of composite laminates with spatially varying system properties. Most of the studies concerning the uncertainty quantification of composites rely on probabilistic analyses, where the prerequisite is to have the statistical distribution of stochastic input parameters. In many engineering problems, these statistical distributions remain unavailable due to the restriction of performing large number of experiments. In such situations, a fuzzy-based approach could be appropriate to characterize the effect of uncertainty. A novel concept of fuzzy representative volume element (FRVE) is developed here for accounting the spatially varying non-probabilistic source-uncertainties at the input level. Such approach of uncertainty modelling is physically more relevant than the prevalent way of modelling non-probabilistic uncertainty without considering the ply-level spatial variability. An efficient radial basis function based stochastic algorithm coupled with the fuzzy finite element model of composites is developed for the multi-scale uncertainty propagation involving multi-synchronous triggering parameters. The concept of a fuzzy factor of safety (FFoS) is discussed in this paper for evaluation of safety factor in the non-probabilistic regime. The results reveal that the present physically relevant approach of modelling fuzzy uncertainty considering plylevel spatial variability obtains significantly lower fuzzy bounds of the global responses compared to the conventional approach of non-probabilistic modelling neglecting the spatially varying attributes.

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Section: Theoretical Formulation For Fuzzy Finite Element Analysis Of Compositesmentioning

confidence: 99%

Spatially varying fuzzy multi-scale uncertainty propagation in unidirectional fibre reinforced composites

Naskar

Mukhopadhyay

Sriramula

2019

Composite Structures

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“…The dynamic characteristics of the "journal-fluid-bearing" tribological system are frequently linked to the behavior of a mechanical model consisting in springs and dashpots [13,29]. According to this model, the system would be described by eight coefficients: four for elasticity or stiffness (k ij ) corresponding to direct (k  , k yy ) and cross-coupling (k y , k y ,) springs, and four for damping (c ij ) associated to direct (c  , c yy ) and cross-coupling (c y , c y )…”

Section: Dynamic Coefficientsmentioning

confidence: 99%

“…13 present the dimensionless damping coefficients as a function of eccentricity for the four L/D considered in this work. As expected, the predictions of the P&O are very similar to those of the ISJB solution at L/D=1/4 for all eccentricities.…”

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confidence: 99%

Dynamic coefficients of finite length journal bearing. Evaluation using a regular perturbation method

Merelli

Barilá

Vignolo

et al. 2019

International Journal of Mechanical Sciences

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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  New simple analytical expressions of static and dynamic parameters are presented  Regular perturbation method allows to extend the Ocvirk solution up to L/D and ~3/4  Force, friction factor, lubricant flow rate and attitude angle are considered  Damping and stiffness coefficients are predicted as a function of L/D and   The proposed method stands as a powerful tool to use in design and analysis of HJBs

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“…In this section, a brief overview is given for the surrogate modelling approach on the basis of high dimensional model representation (HDMR) coupled with the diffeomorphic modulation under observable response preserving homotopy (DMORPH) algorithm. In general, the surrogate models (Dey et al (2016a(Dey et al ( , 2016f, 2018, Mukhopadhyay (2019), , Karsh et al (2018b), Maharshi et al (2018), Mahata et al (2016), Metya et al (2017)) are employed to reduce the number of function evaluations based on actual simulation/ experimental models in a Monte Carlo simulation (refer to figure 4) or a process involving iterative simulations (such as optimization), which need large number of realizations corresponding to random set of input parameters. The surrogate models can encompass any prospective combination of all the input variables within the analysis domain.…”

Section: Hdmr Based Surrogate Modelling Coupled With Dmorph Algorithmmentioning

confidence: 99%

Probabilistic micromechanical spatial variability quantification in laminated composites

Naskar

Mukhopadhyay

Sriramula

2018

Composites Part B: Engineering

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This article presents a probabilistic framework to characterize the dynamic and stability parameters of composite laminates with spatially varying micro and macro-mechanical system properties. A novel approach of stochastic representative volume element (SRVE) is developed in the context of two dimensional plate-like structures for accounting the correlated spatially varying properties. The physically relevant random field based uncertainty modelling approach with spatial correlation is adopted in this paper on the basis of Karhunen-Loève expansion. An efficient coupled HDMR and DMORPH based stochastic algorithm is developed for composite laminates to quantify the probabilistic characteristics in global responses. Convergence of the algorithm for probabilistic dynamics and stability analysis of the structure is verified and validated with respect to direct Monte Carlo simulation (MCS) based on finite element method. The significance of considering higher buckling modes in a stochastic analysis is highlighted. Sensitivity analysis is performed to ascertain the relative importance of different macromechanical and micromechanical properties. The importance of incorporating source-uncertainty in spatially varying micromechanical material properties is demonstrated numerically. The results reveal that stochasticity (/ system irregularity) in material and structural attributes influences the system performance significantly depending on the type of analysis and the adopted uncertainty modelling approach, affirming the necessity to consider different forms of source-uncertainties during the analysis to ensure adequate safety, sustainability and robustness of the structure.

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Stochastic dynamic behaviour of hydrodynamic journal bearings including the effect of surface roughness

Cited by 52 publications

References 74 publications

Spatially varying fuzzy multi-scale uncertainty propagation in unidirectional fibre reinforced composites

Spatially varying fuzzy multi-scale uncertainty propagation in unidirectional fibre reinforced composites

Dynamic coefficients of finite length journal bearing. Evaluation using a regular perturbation method

Probabilistic micromechanical spatial variability quantification in laminated composites

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