This paper presents a stochastic approach to study the natural frequencies of thin-walled laminated composite beams with spatially varying matrix cracking damage in a multi-scale framework. A novel concept of stochastic representative volume element (SRVE) is introduced for this purpose. An efficient radial basis function (RBF) based uncertainty quantification algorithm is developed to quantify the probabilistic variability in free vibration responses of the structure due to spatially random stochasticity in the micro-mechanical and geometric properties. The convergence of the proposed algorithm for stochastic natural frequency analysis of damaged thin-walled composite beam is verified and validated with original finite element method (FEM) along with traditional Monte Carlo simulation (MCS). Sensitivity analysis is carried out to ascertain the relative influence of different stochastic input parameters on the natural frequencies. Subsequently the influence of noise is investigated on radial basis function based uncertainty quantification algorithm to account for the inevitable variability for practical field applications. The study reveals that stochasticity/ system irregularity in structural and material attributes affects the system performance significantly. To ensure robustness, safety and sustainability of the structure, it is very crucial to consider such forms of uncertainties during the analysis.

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This paper presents a generic multivariate adaptive regression splines-based approach for dynamics and stability analysis of sandwich plates with random system parameters. The propagation of uncertainty in such structures has significant computational challenges due to inherent structural complexity and high dimensional space of input parameters. The theoretical formulation is developed based on a refined C0 stochastic finite element model and higher-order zigzag theory in conjunction with multivariate adaptive regression splines. A cubical function is considered for the in-plane parameters as a combination of a linear zigzag function with different slopes at each layer over the entire thickness while a quadratic function is assumed for the out-of-plane parameters of the core and constant in the face sheets. Both individual and combined stochastic effect of skew angle, layer-wise thickness, and material properties (both core and laminate) of sandwich plates are considered in this study. The present approach introduces the multivariate adaptive regression splines-based surrogates for sandwich plates to achieve computational efficiency compared to direct Monte Carlo simulation. Statistical analyses are carried out to illustrate the results of the first three stochastic natural frequencies and buckling load.

This paper presents the effect of noise on surrogate based stochastic natural frequency analysis of composite laminates. Surrogate based uncertainty quantification has gained immense popularity in recent years due to its computational efficiency. On the other hand, noise is an inevitable factor in every real-life design process and structural response monitoring for any practical system. In this study, a novel algorithm is developed to explore the effect of noise in surrogate based uncertainty quantification approaches. The representative results have been presented for stochastic frequency analysis of spherical composite shallow shells considering Kriging based surrogate model. The finite element formulation for laminated composite shells has been developed based on Mindlin's theory considering transverse shear deformation. The proposed approach for quantifying the effect of noise is general in nature and therefore, it can be extended to explore other surrogates under the influence of noise.

Developing a printed elastomeric wearable sensor with good conformity and proper adhesion to skin, coupled with the capability of monitoring various physiological parameters, is very crucial for the development of point-of-care sensing devices with high precision and sensitivity. While there have been previous reports on the fabrication of elastomeric multifunctional sensors, research on the printable elastomeric multifunctional adhesive sensor is not very well explored. Herein, we report the development of a stencil printable multifunctional adhesive sensor fabricated in a solvent-free condition, which demonstrated the capability of having good contact with skin and its ability to function as a temperature and strain sensor. Functionalized liquid isoprene rubber was selected as the matrix while carboxylated multiwalled carbon nanotubes (c-CNTs) were used as the nanofiller. The selection of the above model compounds facilitated the printability and also helped the same composition to demonstrate stretchability and adhesiveness. A realistic three-dimensional microstructure (representative volume element model) was generated through a computational framework for the current c-CNT-liquid elastomer. Further computational simulations were performed to test and validate the correlation between electrical responses to that of experimental studies. Various physiological parameters like motion sensing, pulse, respiratory rate, and phonetics detection were detected by leveraging the electrically resistive nature of the sensor. This development route can be extended toward developing different innovative adhesives for point-of-care sensing applications.