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International audience; Advancements in machine learning have sparked significant interest in designing mechanical metamaterials, i.e., materials that derive their properties from their inherent microstructure rather than just their constituent material. We propose a data-driven exploration of the design space of growth-based cellular metamaterials based on star-shaped distances. These two-dimensional metamaterials are based on periodically-repeating unit cells consisting of material and void patterns with non-trivial geometries. Machine learning models exploiting large datasets are then employed to inverse design growth-based metamaterials for tailored anisotropic stiffness. Firstly, a forward model is created to bypass the growth and homogenization process and accurately predict the mechanical properties given a finite set of design parameters. Secondly, an inverse model is used to invert the structure–property maps and enable the accurate prediction of designs for a given anisotropic stiffness query. We successfully demonstrate the frameworks’ generalization capabilities by inverse designing for stiffness properties chosen from outside the domain of the design space.

Surgical resection of meningioma leaves residual solid tumour in over 25% of patients. Selection for further treatment and follow-up strategy may benefit from knowledge of volumetric growth and factors associated with re-growth. The aim of this review was to evaluate volumetric growth and variables associated with growth in patients that underwent incomplete resection of a meningioma without the use of adjuvant radiotherapy. A systematic review was conducted in accordance with the PRISMA statement and registered a priori with PROSPERO (registration number: CRD42020177052). Six databases were searched up to May 2020. Full text articles analysing volumetric growth rates in at least 10 patients who had residual meningioma after surgery were assessed. Four single-centre, retrospective studies totalling 238 patients were included, of which 99% of meningioma were WHO grade 1. The absolute tumour growth rate ranged from 0.09 to 4.94 cm3 per year. The relative growth rate ranged from 5.11 to 14.18% per year. Varying methods of volumetric assessment and definitions of growth impeded pooled analysis. Pre-operative and residual tumour volume, and hyperintensity on T2 weighted MRI were identified as variables associated with residual meningioma growth, however this was inconsistent across studies. Risk of bias was high in all studies. Radiological regrowth occurred in 42–67% of cases. Our review identified that volumetric growth of residual meningioma is scarcely reported. Sufficiently powered studies are required to delineate volumetric growth and prognostic factors to stratify management.

We extend EUCLID, a computational strategy for automated material model discovery and identification, to linear viscoelasticity. For this case, we perform a priori model selection by adopting a generalized Maxwell model expressed by a Prony series, and deploy EUCLID for identification. The methodology is based on four ingredients: i. full-field displacement and net force data; ii. a very wide material model library — in our case, a very large number of terms in the Prony series; iii. the linear momentum balance constraint; iv. the sparsity constraint. The devised strategy comprises two stages. Stage 1 relies on sparse regression; it enforces momentum balance on the data and exploits sparsity-promoting regularization to drastically reduce the number of terms in the Prony series and identify the material parameters. Stage 2 relies on k-means clustering; starting from the reduced set of terms from stage 1, it further reduces their number by grouping together Maxwell elements with very close relaxation times and summing the corresponding moduli. Automated procedures are proposed for the choice of the regularization parameter in stage 1 and of the number of clusters in stage 2. The overall strategy is demonstrated on artificial numerical data, both without and with the addition of noise, and shown to efficiently and accurately identify a linear viscoelastic model with five relaxation times across four orders of magnitude, out of a library with several hundreds of terms spanning relaxation times across seven orders of magnitude. ISSN:0167-6636 ISSN:1872-7743

The Krishna Godavari offshore basin to the east of India is a proven reserve, rich in natural gas hydrate. The impact of sediment characteristics on the hydrate formation has already been well established in open literature. In the present study, we have attempted to investigate the mineralogy of sediments collected from KG basin by elemental analysis (energy dispersive spectroscopy) and x-ray diffraction. Various physicochemical characteristics of the clayey sediments such as TDS, salinity, pH, conductivity and resistivity were analysed at various concentrations and compared together. Also, this paper highlights the rheological behavior of the sediment samples with concentrations of 20, 35 and 50 wt% at different experimental temperatures (278.15 K, 283.15 K and 288.15 K). Viscosity measurements were performed for a wide range of shear rates for all concentrations and comparative studies have been conducted on their exhibited behavior. The viscosity of sediment sample were found to be varying from 76.3 to 0.003 Pa s depending on the sediment concentration, temperature and shear rate. In addition, viscoelastic measurements were carried out at various angular frequencies for all the sediment samples. The work aims to characterise the sediments of KG basin and analyse the rheological behavior of sediment solution which has not yet been reported in open literature. This will provide vital information for possible methane recovery from KG basin hydrate reservoir by manipulating the host sediment behavior.

Natural gas hydrate is a potential source of methane which needs to be extracted from under the sea bed. For the economic recovery of methane from natural gas hydrates, production approaches such as depressurization, thermal stimulation, and inhibitor injection are being investigated. However, studies involving hydrate-bearing clayey sediments and recovery of methane from such reservoirs are rare. This work investigates in detail the potency of hydrate dissociation methods such as depressurization by constant rate gas release, thermal stimulation and the combination of two for energy recovery from hydrate bearing clayey sediments underlying a free gas zone. Pure water and two different mud samples containing 3 and 5 wt% of bentonite were used for methane hydrate formation and dissociation studies. Thermodynamic study of methane hydrate in the presence of bentonite clay was also conducted for the above two concentrations. No considerable effect of clay on the inhibition or promotion of methane hydrate formation was observed. Initially, methane hydrate formation has been investigated using pure water, 3 and 5 wt% bentonite mud at an initial hydrate formation pressure of 8 MPa and at a temperature of 278.15 K. Subsequently, methane hydrate dissociation experiments were carried out using depressurization, thermal stimulation and their combination. The effect of the rate of gas release on hydrate dissociation by depressurization was investigated using two different rates of 10 mL/min and 20 mL/min. Thermal stimulation experiments were carried out for ΔT = 15 K at the rate of 7.5 K/hr and the results on methane recovery were recorded. The detailed investigation shows that the combination of the two methods is more efficient for methane production than the standalone method in clayey hydrate reservoir. This study provides important insights into the hydrate production methodology from clayey hydrate reservoirs.

We present a two-scale topology optimization framework for the design of macroscopic bodies with an optimized elastic response, which is achieved by means of a spatially-variant cellular architecture on the microscale. The chosen spinodoid topology for the cellular network on the microscale (which is inspired by natural microstructures forming during spinodal decomposition) admits a seamless spatial grading as well as tunable elastic anisotropy, and it is parametrized by a small set of design parameters associated with the underlying Gaussian random field. The macroscale boundary value problem is discretized by finite elements, which in addition to the displacement field continuously interpolate the microscale design parameters. By assuming a separation of scales, the local constitutive behavior on the macroscale is identified as the homogenized elastic response of the microstructure based on the local design parameters. As a departure from classical FE2 -type approaches, we replace the costly microscale homogenization by a data-driven surrogate model, using deep neural networks, which accurately and efficiently maps design parameters onto the effective elasticity tensor. The model is trained on homogenized stiffness data obtained from numerical homogenization by finite elements. As an added benefit, the machine learning setup admits automatic differentiation, so that sensitivities (required for the optimization problem) can be computed exactly and without the need for numerical derivatives – a strategy that holds promise far beyond the elastic stiffness. Therefore, this framework presents a new opportunity for multiscale topology optimization based on data-driven surrogate models., Computer Methods in Applied Mechanics and Engineering, 383, ISSN:0045-7825, ISSN:1879-2138

We propose a new approach for data-driven automated discovery of isotropic hyperelastic constitutive laws. The approach is unsupervised, i.e., it requires no stress data but only displacement and global force data, which are realistically available through mechanical testing and digital image correlation techniques; it delivers interpretable models, i.e., models that are embodied by parsimonious mathematical expressions discovered through sparse regression of a large catalogue of candidate functions; it is one-shot, i.e., discovery only needs one experiment — but can use more if available. The problem of unsupervised discovery is solved by enforcing equilibrium constraints in the bulk and at the loaded boundary of the domain. Sparsity of the solution is achieved by ℓp regularization combined with thresholding, which calls for a non-linear optimization scheme. The ensuing fully automated algorithm leverages physics-based constraints for the automatic determination of the penalty parameter in the regularization term. Using numerically generated data including artificial noise, we demonstrate the ability of the approach to accurately discover five hyperelastic models of different complexity. We also show that, if a “true” feature is missing in the function library, the proposed approach is able to surrogate it in such a way that the actual response is still accurately predicted., Computer Methods in Applied Mechanics and Engineering, 381, ISSN:0045-7825, ISSN:1879-2138

Severe plastic deformation (SPD), occurring ubiquitously across metal forming processes, has been utilized to significantly improve bulk material properties such as the strength of metals. The latter is achieved by ultra-fine grain refinement at the polycrystalline mesoscale via the application of large plastic strains on the macroscale. We here present a multiscale framework that aims at efficiently modeling SPD processes while effectively capturing the underlying physics across all relevant scales. At the level of the macroscale boundary value problem, an enhanced maximum-entropy (max-ent) meshless method is employed. Compared to finite elements and other meshless techniques, this method offers a stabilized finite-strain updated-Lagrangian setting for improved robustness with respect to mesh distortion arising from large plastic strains. At each material point on the macroscale, we describe the polycrystalline material response via a Taylor model at the mesoscale, which captures discontinuous dynamic recrystallization through the nucleation and growth/shrinkage of grains. Each grain, in turn, is modeled by a finite-strain crystal plasticity model at the microscale. We focus on equal-channel angular extrusion (ECAE) of polycrystalline pure copper as an application, in which severe strains are generated by extruding the specimen around a 90 ° -corner. Our framework describes not only the evolution of strain and stress distributions during the process but also grain refinement and texture evolution, while offering a computationally feasible treatment of the macroscale mechanical boundary value problem. Though we here focus on ECAE of copper, the numerical setup is sufficiently general for other applications including SPD and thermo-mechanical processes (e.g., rolling, high-pressure torsion, etc.) as well as other materials systems.