Stochastic dynamic analysis of composite plate with random temperature increment

Chandra, S.; Sepahvand, K.; Matsagar, Vasant; Marburg, Steffen

doi:10.1016/j.compstruct.2019.111159

Cited by 23 publications

(7 citation statements)

References 48 publications

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“…This, in turn, decreases the stiffness of the composite structure, i.e., with an increase in temperature and moisture, the stiffness of the composite structure decreases. As the stiffness of any system is directly proportional to the natural frequency of that system, the natural frequency of the structure decreases with an increase in temperature and moisture concentration values [20,21,38].…”

Section: Model Simulation Resultsmentioning

confidence: 99%

“…Several simulations were performed to understand the influence of the length to thickness ratio, loading profile (uniformly distributed and sinusoidal), modular ratio, boundary conditions, thermal and moisture coefficients, etc., on the central deflection of the structure. Chandra et al [20] performed a stochastic dynamic response investigation of composite plates using generalized polynomial chaos expansion for random temperature increments. Rath and Sahu [21] conducted a numerical and experimental investigation of the impact of the hygrothermal environment on the vibration response of composite plates.…”

Section: Introductionmentioning

confidence: 99%

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Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates

et al. 2021

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The present study deals with the development of a prediction model to investigate the impact of temperature and moisture on the vibration response of a skew laminated composite sandwich (LCS) plate using the artificial neural network (ANN) technique. Firstly, a finite element model is generated to incorporate the hygro-elastic and thermo-elastic characteristics of the LCS plate using first-order shear deformation theory (FSDT). Graphite-epoxy composite laminates are used as the face sheets, and DYAD606 viscoelastic material is used as the core material. Non-linear strain-displacement relations are used to generate the initial stiffness matrix in order to represent the stiffness generated from the uniformly varying temperature and moisture concentrations. The mechanical stiffness matrix is derived using linear strain-displacement associations. Then the results obtained from the numerical model are used to train the ANN. About 11,520 data points were collected from the numerical analysis and were used to train the network using the Levenberg–Marquardt algorithm. The developed ANN model is used to study the influence of various process parameters on the frequency response of the system, and the outcomes are compared with the results obtained from the numerical model. Several numerical examples are presented and conferred to comprehend the influence of temperature and moisture on the LCS plates.

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Section: Model Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates

et al. 2021

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“…The collocation method is adopted [42] to determine the unknown coefficients y i in Equation (31). In this method, the numerical model is considered a black box.…”

Section: Calculation Of Unknown Coefficients Using the Collocation Me...mentioning

confidence: 99%

“…Once the order of the gPC expansion is known, the collocation points are generated as all combinations of roots of the next-higherorder polynomial basis. The origin (i.e., ξ = 0) is added if it is not already included in the roots [42]. It is assumed that a set of response samples Y = {Y 1 , Y 2 , .…”

Section: Calculation Of Unknown Coefficients Using the Collocation Me...mentioning

confidence: 99%

“…To achieve better computational efficiency, various non-sampling-based methods, such as generalized polynomial chaos (gPC) expansion, have received much attention in recent years [38][39][40]. In the gPC expansion method, the uncertain parameters are represented by a series of orthogonal polynomials with unknown coefficients [41,42]. Recently, Garoli et al [43] demonstrated the application of the gPC expansion in a stochastic dynamic response analysis of a nonlinear rotor-bearing system, in which the nonlinear bearing forces are obtained using the IS model.…”

Section: Introductionmentioning

confidence: 99%

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Stability Analysis of Rotor-Bearing Systems under the Influence of Misalignment and Parameter Uncertainty

2021

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Stability is a well-known challenge for rotating systems supported by hydrodynamic bearings (HDBs), particularly for the condition where the misalignment effect and the parametric uncertainty are considered. This study investigates the impact of misalignment and inherent uncertainties in bearings on the stability of a rotor-bearing system. The misalignment effect is approximately described by introducing two misaligned angles. The characteristics of an HDB, such as pressure distribution and dynamic coefficients, are calculated by the finite difference method (FDM). The stability threshold is evaluated as the intersection of run-up curve and borderline. Viscosity and clearance are considered as uncertain parameters. The generalized polynomial chaos (gPC) expansion is adopted to quantify the uncertainty in parameters by evaluating unknown coefficients. The unknown gPC coefficients are obtained by using the collocation method. The results obtained by the gPC expansion are compared with those of the Monte Carlo (MC) simulation. The results show that the characteristics of the HDB and the stability threshold are affected by misalignment and parameter uncertainties. As the uncertainty analysis using the gPC expansion is performed on a relatively small number of predefined collocation points compared with the large number of MC samples, the method is very efficient in terms of computation time.

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Stochastic Modal Damping Analysis of Stiffened Laminated Composite Plate

Chandra

Sepahvand

Matsagar

et al. 2020

Lecture Notes in Civil Engineering

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Stochastic dynamic analysis of composite plate with random temperature increment

Cited by 23 publications

References 48 publications

Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates

Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates

Stability Analysis of Rotor-Bearing Systems under the Influence of Misalignment and Parameter Uncertainty

Stochastic Modal Damping Analysis of Stiffened Laminated Composite Plate

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