Buckling Behavior of Thick Porous FGM Toroidal Shell Segments Under External Pressure and Elevated Temperature Including Tangential Edge Restraint

Owing to mathematical and geometrical complexities, there is an evident lack of stability analyses of thick closed shell structures with porosity. The present work aims to analyze the effects of porosities, elasticity of edge constraint and surrounding elastic media on the buckling resistance capacity of thick functionally graded material (FGM) toroidal shell segments subjected to external pressure, elevated temperature and the combined action of these loads. The volume fractions of constituents are varied across the thickness according to power law functions and effective properties of the FGM are determined using a modified rule of mixture. The porosities exist in the FGM through even and uneven distributions. Governing equations are based on a higher order shear deformation theory taking into account interactive pressure from surrounding elastic media. These equations are analytically solved and closed-form expressions of buckling loads are derived adopting the two-term form of deflection along with Galerkin method. Parametric studies indicate that the porosities have beneficial and deteriorative influences on the buckling resistance capacity of thermally loaded and pressure loaded porous FGM toroidal shell segments, respectively. Furthermore, tangential constraints of edges lower the buckling resistance capacity of the shells, especially at elevated temperatures.

Numerical investigation of sensing and energy harvesting performance of 0-3 and triply periodic minimal surface-based K0.475Na0.475Li0.05(Nb0.92Ta0.05Sb0.03)O3 and polyethylene piezocomposite: A comparative study

The present paper is devoted to conducting a comparative study on the sensing and energy harvesting performance of a 0-3 and triply periodic minimal surface (TPMS)-based piezocomposite with [Formula: see text] ( KNLNTS) material as piezoelectric inclusions and polyethylene as the matrix material. Different types of TPMS are reported in literature like Neovius, Fischer-Koch S, Schwarz CLP, Schoen Gyroid, Schoen IWP, Schwarz Primitive, etc. In the present study, Schwarz primitive TPMS is considered. Representative volume elements (RVEs) with four different volume fractions are generated and the finite element simulations are performed to compute the effective elastic and piezoelectric properties. The homogenization technique is used to calculate the effective properties. The calculated values of the effective properties are further used to calculate the sensing voltage between the electrodes and harvested power across the resistance. The effective elastic and piezoelectric properties increase with an increase in volume fraction of the piezoelectric inclusions resulting in a higher sensing voltage and power. Significant improvement in the effective elastic and piezoelectric properties of TPMS-based piezocomposite was observed. TPMS-based piezocomposite exhibited superior performance as compared to their 0-3 counterparts.

Large-Scale Statistical Learning for Mass Transport Prediction in Porous Materials Using 90,000 Artificially Generated Microstructures

Effective properties of functional materials crucially depend on their 3D microstructure. In this paper, we investigate quantitative relationships between descriptors of two-phase microstructures, consisting of solid and pores and their mass transport properties. To that end, we generate a vast database comprising 90,000 microstructures drawn from nine different stochastic models, and compute their effective diffusivity and permeability as well as various microstructural descriptors. To the best of our knowledge, this is the largest and most diverse dataset created for studying the influence of 3D microstructure on mass transport. In particular, we establish microstructure-property relationships using analytical prediction formulas, artificial (fully-connected) neural networks, and convolutional neural networks. Again, to the best of our knowledge, this is the first time that these three statistical learning approaches are quantitatively compared on the same dataset. The diversity of the dataset increases the generality of the determined relationships, and its size is vital for robust training of convolutional neural networks. We make the 3D microstructures, their structural descriptors and effective properties, as well as the code used to study the relationships between them available open access.

Analysis of unbalanced laminate composites with imperfect interphase: Effective properties via asymptotic homogenization method

This work addresses the Asymptotic Homogenization Method (AHM) to find all the non-zero independent constants of the fourth-order elasticity tensor of a theoretically infinite periodically laminated composite. The concept of Unit Cell describes the domain, comprised of two orthotropic composite plies separated by an isotropic interphase. A general case with an unbalanced composite is considered. Thus, the coupled components of the tensor are expected. Both analytical and numerical solutions are derived. In addition, an interphase degradation model is proposed to evaluate its effect on the effective properties of the media. Two different stacking sequences are considered with five degrees of interphase imperfection each. The results show good agreement between the analytical and numerical solutions. In addition, it is clear that the more imperfect the interphase is, the more affected the effective properties of the media are, especially those dependent on the stacking direction.

Nonlinear response of doubly curved sandwich panels with CNT-reinforced composite core and elastically restrained edges subjected to external pressure in thermal environments

An analytical investigation on the nonlinear response of doubly curved panels constructed from homogeneous face sheets and carbon nanotube reinforced composite (CNTRC) core and subjected to external pressure in thermal environments is presented in this paper. Carbon nanotubes (CNTs) are reinforced into the core layer through uniform or functionally graded distributions. The properties of constituents are assumed to be temperature dependent and effective properties of CNTRC are determined using an extended rule of mixture. Governing equations are established within the framework of first order shear deformation theory taking into account geometrical imperfection, von Kármán–Donnell nonlinearity, panel-foundation interaction and elasticity of tangential edge restraints. These equations are solved using approximate analytical solutions and Galerkin method for simply supported panels. The results reveal that load carrying capacity of sandwich panels is stronger when boundary edges are more rigorously restrained and face sheets are thicker. Furthermore, elevated temperature has deteriorative and beneficial influences on the load bearing capability of sandwich panels with movable and restrained edges, respectively.

MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra

The ‘inverse problem’ of mass spectrometric molecular identification (‘given a mass spectrum, calculate/predict the 2D structure of the molecule whence it came’) is largely unsolved, and is especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number of experimentally available electrospray mass spectra of small molecules is quite limited. However, the forward problem (‘calculate a small molecule’s likely fragmentation and hence at least some of its mass spectrum from its structure alone’) is much more tractable, because the strengths of different chemical bonds are roughly known. This kind of molecular identification problem may be cast as a language translation problem in which the source language is a list of high-resolution mass spectral peaks and the ‘translation’ a representation (for instance in SMILES) of the molecule. It is thus suitable for attack using the deep neural networks known as transformers. We here present MassGenie, a method that uses a transformer-based deep neural network, trained on ~6 million chemical structures with augmented SMILES encoding and their paired molecular fragments as generated in silico, explicitly including the protonated molecular ion. This architecture (containing some 400 million elements) is used to predict the structure of a molecule from the various fragments that may be expected to be observed when some of its bonds are broken. Despite being given essentially no detailed nor explicit rules about molecular fragmentation methods, isotope patterns, rearrangements, neutral losses, and the like, MassGenie learns the effective properties of the mass spectral fragment and valency space, and can generate candidate molecular structures that are very close or identical to those of the ‘true’ molecules. We also use VAE-Sim, a previously published variational autoencoder, to generate candidate molecules that are ‘similar’ to the top hit. In addition to using the ‘top hits’ directly, we can produce a rank order of these by ‘round-tripping’ candidate molecules and comparing them with the true molecules, where known. As a proof of principle, we confine ourselves to positive electrospray mass spectra from molecules with a molecular mass of 500Da or lower, including those in the last CASMI challenge (for which the results are known), getting 49/93 (53%) precisely correct. The transformer method, applied here for the first time to mass spectral interpretation, works extremely effectively both for mass spectra generated in silico and on experimentally obtained mass spectra from pure compounds. It seems to act as a Las Vegas algorithm, in that it either gives the correct answer or simply states that it cannot find one. The ability to create and to ‘learn’ millions of fragmentation patterns in silico, and therefrom generate candidate structures (that do not have to be in existing libraries) directly, thus opens up entirely the field of de novo small molecule structure prediction from experimental mass spectra.

Earthquake source characterization by machine learning algorithms applied to acoustic signals

Underwater seismic events generate acoustic radiation (such as acoustic-gravity waves), that carries information about the source and can travel long distances before dissipating. Effective early warning, emergency response, and information dissemination for earthquakes and tsunamis require a rapid characterisation of the fault properties: geometry and dynamics. In this work, we analysed hydrophone recordings of 201 earthquakes, located in the Pacific and the Indian Ocean, by employing acoustic signal processing and classification methods. The analysis allows identifying the type of earthquake (i.e. slip type, magnitude) and provides near real-time estimation of the effective properties of the fault dynamics and geometry. The results were compared against values reported by the Harvard Global Centroid Moment Tensor catalog (gCMT), revealing statistical significance between the extracted acoustic properties used to feed machine learning algorithms and the predicted slip and magnitude values.

Effective in-plane elastic properties of honeycomb-polymer composites

Honeycomb composites are now common materials in applications where high specific stiffness is required. Previous research has found that honeycombs with polymer infills in their cells, here referred to as honeycomb-polymer composites (HPCs), exhibit effective stiffnesses greater than the honeycomb or polymer alone. Currently, the state of analytic models for predicting the elastic properties of these composites is limited, and further research is needed to better characterize the behavior of these materials. In this research, a nonlinear finite element analysis was employed to perfor2m parametric studies of a filled honeycomb unit cell with isotropic wall and infill materials. A rigid wall model was created as an upper bound on the deformable wall model’s performance, and an empty honeycomb model was employed to better understand the mechanisms of stiffness amplification. Parametric studies were completed for infill material properties and cell geometry, with the effective Young’s modulus studied in two in-plane material directions. The mechanisms by which the stiffness amplification occurs are studied, and comparisons to existing analytic models are made. It has been observed that both the volume change within the honeycomb cell under deformation and the mismatch in Poisson’s ratios between the honeycomb and infill influence the effective properties. Stiffness amplifications of over 4000 have been observed, with auxetic behavior achieved by tailoring of the HPC geometry. Additionally, the effect of large effective strains up to 10% is explored, where the cell geometry changes significantly. This research provides an important step toward understanding the design space and benefits of HPCs.