Design of ultra-thin composite deployable shell structures through machine learning

A data-driven computational framework is applied for the design of optimal ultra-thin Triangular Rollable and Collapsible (TRAC) carbon fiber booms. High-fidelity computational analyses of a large number of geometries are used to build a database. This database is then analyzed by machine learning to construct design charts that are shown to effectively guide the design of the ultra-thin deployable structure. The computational strategy discussed herein is general and can be applied to different problems in structural and materials design, with the potential of finding relevant designs within high-dimensional spaces

Characterization of Ultra-Thin Composite Triangular Rollable and Collapsible Booms

The paper studies the behavior of Triangular Rollable and Collapsible (TRAC) booms made from ultra-thin carbon fiber, with a total flange thickness of 71 µm. Both bending and torsional behavior of the deployed booms are studied using numerical analysis and experimental testing. The coiling of the booms around hubs of large radius is also studied

Characterization of Ultra-Thin Composite Triangular Rollable and Collapsible Booms

The paper studies the behavior of Triangular Rollable and Collapsible (TRAC) booms made from ultra-thin carbon fiber, with a total flange thickness of 71 µm. Both bending and torsional behavior of the deployed booms are studied using numerical analysis and experimental testing. The coiling of the booms around hubs of large radius is also studied

iPINNs: Incremental learning for Physics-informed neural networks

Physics-informed neural networks (PINNs) have recently become a powerful tool for solving partial differential equations (PDEs). However, finding a set of neural network parameters that lead to fulfilling a PDE can be challenging and non-unique due to the complexity of the loss landscape that needs to be traversed. Although a variety of multi-task learning and transfer learning approaches have been proposed to overcome these issues, there is no incremental training procedure for PINNs that can effectively mitigate such training challenges. We propose incremental PINNs (iPINNs) that can learn multiple tasks (equations) sequentially without additional parameters for new tasks and improve performance for every equation in the sequence. Our approach learns multiple PDEs starting from the simplest one by creating its own subnetwork for each PDE and allowing each subnetwork to overlap with previously learned subnetworks. We demonstrate that previous subnetworks are a good initialization for a new equation if PDEs share similarities. We also show that iPINNs achieve lower prediction error than regular PINNs for two different scenarios: (1) learning a family of equations (e.g., 1-D convection PDE); and (2) learning PDEs resulting from a combination of processes (e.g., 1-D reaction-diffusion PDE). The ability to learn all problems with a single network together with learning more complex PDEs with better generalization than regular PINNs will open new avenues in this field

Resistance Curves in the Tensile and Compressive Longitudinal Failure of Composites

This paper presents a new methodology to measure the crack resistance curves associated with fiber-dominated failure modes in polymer-matrix composites. These crack resistance curves not only characterize the fracture toughness of the material, but are also the basis for the identification of the parameters of the softening laws used in the analytical and numerical simulation of fracture in composite materials. The method proposed is based on the identification of the crack tip location by the use of Digital Image Correlation and the calculation of the J-integral directly from the test data using a simple expression derived for cross-ply composite laminates. It is shown that the results obtained using the proposed methodology yield crack resistance curves similar to those obtained using FEM-based methods in compact tension carbon-epoxy specimens. However, it is also shown that the Digital Image Correlation based technique can be used to extract crack resistance curves in compact compression tests for which FEM-based techniques are inadequate

High-Strength Amorphous Silicon Carbide for Nanomechanics

For decades, mechanical resonators with high sensitivity have been realized using thin-film materials under high tensile loads. Although there have been remarkable strides in achieving low-dissipation mechanical sensors by utilizing high tensile stress, the performance of even the best strategy is limited by the tensile fracture strength of the resonator materials. In this study, a wafer-scale amorphous thin film is uncovered, which has the highest ultimate tensile strength ever measured for a nanostructured amorphous material. This silicon carbide (SiC) material exhibits an ultimate tensile strength of over 10 GPa, reaching the regime reserved for strong crystalline materials and approaching levels experimentally shown in graphene nanoribbons. Amorphous SiC strings with high aspect ratios are fabricated, with mechanical modes exceeding quality factors 10^8 at room temperature, the highest value achieved among SiC resonators. These performances are demonstrated faithfully after characterizing the mechanical properties of the thin film using the resonance behaviors of free-standing resonators. This robust thin-film material has significant potential for applications in nanomechanical sensors, solar cells, biological applications, space exploration and other areas requiring strength and stability in dynamic environments. The findings of this study open up new possibilities for the use of amorphous thin-film materials in high-performance applications

The effect of through-thickness compressive stress on mode II interlaminar crack propagation:A computational micromechanics approach

A micromechanics framework for modelling the mode II interlaminar crack onset and propagation in fibre-reinforced composites is presented with the aim of i) modelling the micro-scale failure mechanisms that underlie interlaminar crack propagation, and ii) determining the effect of the through-thickness pressure on mode II fracture toughness. An algorithm for the generation of the fibre distribution is proposed for the generation of three-dimensional Representative Volume Elements (RVEs). Appropriate constitutive models are used to model the different dissipative effects that occur at crack propagation. Numerical predictions are compared with experiments obtained in previous investigations: it is concluded that the proposed micromechanical model is able to simulate conveniently the interlaminar crack propagation and to take into account the effect of the through-thickness pressure on mode II interlaminar fracture toughness

A Trans-Dimensional Approach to the Behavioral Aspects of Depression

Depression, a complex mood disorder, displays high comorbidity with anxiety and cognitive disorders. To establish the extent of inter-dependence between these behavioral domains, we here undertook a systematic analysis to establish interactions between mood [assessed with the forced-swimming (FST) and sucrose consumption tests (SCT)], anxiety [elevated-plus maze (EPM) and novelty suppressed feeding (NSF) tests] and cognition (spatial memory and behavioral flexibility tests) in rats exposed to unpredictable chronic-mild-stress (uCMS). Expectedly, uCMS induced depressive-like behavior, a hyperanxious phenotype and cognitive impairment; with the exception of the measure of anxiety in the EPM, these effects were attenuated by antidepressants (imipramine, fluoxetine). Measures of mood by the FST and SCT were strongly correlated, whereas no significant correlations were found between the different measures of anxiety (EPM and NSF); likewise, measures of cognition by spatial memory and behavioral flexibility tests were poorly correlated. Inter-domain analysis revealed significant correlations between mood (FST and SCT) and anxiety-like behavior (NSF, but not EPM). Furthermore, significant correlations were found between cognitive performance (reverse learning task) and mood (FST and SCT) and anxiety-like behavior (NSF). These results demonstrate interactions between different behavioral domains that crosscut the disciplines of psychiatry and neurology