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Interrupted X-ray computed tomography (X-CT) has been the common way to observe the deformation of materials during an experiment. While this approach is effective for quasi-static experiments, it has never been possible to reconstruct a full 3d tomography during a dynamic experiment which cannot be interrupted. In this work, we propose that neural rendering tools can be used to drive the paradigm shift to enable 3d reconstruction during dynamic events. First, we derive theoretical results to support the selection of projections angles. Via a combination of synthetic and experimental data, we demonstrate that neural radiance fields can reconstruct data modalities of interest more efficiently than conventional reconstruction methods. Finally, we develop a spatio-temporal model with spline-based deformation field and demonstrate that such model can reconstruct the spatio-temporal deformation of lattice samples in real-world experiments., Comment: 24 pages, 14 figures. Submitted to NeurIPS 2024 ML4PS. For associated visualizations, see https://neural-xray.github.io/nerfxray

PyCPL provides full access to ESO's Common Pipeline Library ( CPL) for astronomical data reduction within a Python environment. Not only does it offer a Python interface to the robust CPL library, but it also lets users and developers fully utilise the rest of the scientific Python ecosystem. We have written a C++ layer to CPL and with pybind11 (a third-party library) created a Pythonic API to CPL. Since CPL has been around for so long, it has been thoroughly tested and understood. In 2003 it was developed in C due to its efficiency and speed of execution. With the community however moving away from C/C++ programming and embracing Python for data processing tasks, there is a need to provide access to the CPL utilities within a Python environment. With the latest version being released users can now install PyCPL to run existing CPL recipes (written in C) and access the results from Python. It also provides the ability to create new recipes in Python using the functionality provided by CPL., Comment: This paper was for a poster presented in ADASS XXXIII. poster number P923

Additive manufacturing (AM) enables the development of high-performance architected cellular materials, emphasizing the growing importance of establishing programmable and predictable energy absorption capabilities. This study evaluates the impact of a precisely tuned fused filament fabrication (FFF) AM process on the energy absorption and failure characteristics of thermoplastic lattice materials through multiscale experiments and predictive modelling. Lattices with four distinct unit cell topologies and three varying relative densities are manufactured, and their in-plane mechanical response under quasi-static compression is measured. Macroscale testing and micro-CT imaging reveal relative density-dependent damage mechanisms and failure modes, prompting the development of a robust predictive modelling framework to capture process-induced performance variation and damage. For lower relative density lattices, an FE model based on the extended Drucker-Prager material model, incorporating Bridgman correction with crazing failure criteria, accurately captures the crushing response. As lattice density increases, interfacial damage along bead-bead interfaces becomes predominant, necessitating the enrichment of the model with a microscale cohesive zone model to capture interfacial debonding. All proposed models are validated, highlighting inter-bead damage as the primary factor limiting energy absorption performance in FFF-printed lattices. Finally, the predictive modelling introduces an enhancement factor, providing a straightforward approach to assess the influence of the AM process on energy absorption performance, facilitating the inverse design of FFF-printed lattices. This approach enables a critical evaluation of how FFF processes can be improved to achieve the highest attainable performance and mitigate failures in architected cellular materials.

Creep is a generic descriptor of slow motions -- in the context of materials, it describes quasi-static deformation of a solid when subjected to stresses below the global yield, at which all rigidity collapses and the material flows. Here, we experimentally investigate creep, flow, and the transition between the two states in a granular heap flow. Within the surface flowing layer the dimensionless strain rate diminishes with depth, there is an absence of spatial correlations, and there is no aging dynamics. Beneath this layer, the bulk creeps via localized avalanches of plasticity, and there is significant aging. The transition between fast surface flow and slow bulk creep and aging is observed to be in the vicinity of a critical inertial number of $I = 10^{-5}$. Surprisingly, at the cessation of surface flow and the `quenching' of the pile, creep persists in the absence of the flowing layer; albeit with significant differences for a pile that experiences a long duration of surface flow (strongly annealed) and one where flow during preparation does not last long (weakly annealed). Our results contribute to an emerging view of athermal granular creep, showing similarities across dry and submerged systems. Quenched quiescent heaps that creep indefinitely, however, present a challenge to granular rheology, and open new possibilities for interpreting and casting creep and deformation of soils in nature.

Lattices are architected metamaterials whose properties strongly depend on their geometrical design. The analogy between lattices and graphs enables the use of graph neural networks (GNNs) as a faster surrogate model compared to traditional methods such as finite element modelling. In this work, we generate a big dataset of structure-property relationships for strut-based lattices. The dataset is made available to the community which can fuel the development of methods anchored in physical principles for the fitting of fourth-order tensors. In addition, we present a higher-order GNN model trained on this dataset. The key features of the model are (i) SE(3) equivariance, and (ii) consistency with the thermodynamic law of conservation of energy. We compare the model to non-equivariant models based on a number of error metrics and demonstrate its benefits in terms of predictive performance and reduced training requirements. Finally, we demonstrate an example application of the model to an architected material design task. The methods which we developed are applicable to fourth-order tensors beyond elasticity such as piezo-optical tensor etc., Comment: International Conference on Learning Representations 2024

Inspired by presentations by the authors during a workshop organized at the Princeton Center for Theoretical Science (PCTS) in January 2022, we present a perspective on some of the outstanding questions related to the "physics of the ground beneath our feet." These identified challenges are intrinsically shared with the field of Soft Matter but also have unique aspects when the natural environment is studied., Comment: Perspective Paper, 30 pages, 15 figures

The presence of hydrogen traps within a metallic alloy influences the rate of hydrogen diffusion. The electro-permeation (EP) test can be used to assess this: the permeation of hydrogen through a thin metallic sheet is measured by suitable control of hydrogen concentration on the front face and by recording the flux of hydrogen that exits the rear face. Additional insight is achieved by the more sophisticated three stage EP test: the concentration of free lattice hydrogen on the front face is set to an initial level, is then dropped to a lower intermediate value and is then restored to the initial level. The flux of hydrogen exiting the rear face is measured in all three stages of the test. In the present study, a transient analysis is performed of hydrogen permeation in a three stage EP test, assuming that lattice diffusion is accompanied by trapping and de-trapping. The sensitivity of the three stage EP response to the depth and density of hydrogen traps is quantified. A significant difference in permeation response can exist between the first and third stages of the EP test when the alloy contains a high number density of deep traps.

The present paradigm in design and modelling of lattice architected mechanical metamaterials is mostly limited to traditional numerical methods like finite element analysis. Recently, the use of machine learning and artificial intelligence techniques have become popular and here we extend these ideas to architected metamaterials. We show that truss based lattices have a natural resemblance to computational graphs which serve as an input for the rapidly emerging field of graph neural networks (GNNs). A dataset comprising thousands of such extremely complex lattices is trained using a GNN to predict the underlying dominant deformation mechanism viz. stretching and bending. The trained GNN achieves > 90% accuracy on a previously unseen complex lattice dataset. Such graph-based learning of metamaterials has the capability to predict a range of properties, from elastic moduli to fracture toughness and promises AI driven discovery of emergent metamaterials possessing superlative properties., Comment: 7 pages, 6 figures

Rapid progress in additive manufacturing methods has created a new class of ultralight and strong architected metamaterials that resemble periodic truss structures. The mechanical performance of these metamaterials with a very large number of unit cells is ultimately limited by their tolerance to damage and defects, but an understanding of this sensitivity has remained elusive. Using a stretching-dominated micro-architecture and metamaterial specimens comprising millions of unit-cells we show that not only is the stress intensity factor, as used in conventional elastic fracture mechanics, insufficient to characterize fracture but also that conventional fracture testing protocols are inadequate. Via a combination of numerical calculations and asymptotic analyses, we extend the ideas of fracture mechanics and develop a general test and design protocol for the failure of metamaterials., Comment: 58 pages, 5 figures, Supplementary Information, 4 Supplementary Movies

Recent advances in additive manufacturing methods make it possible, for the first time, to manufacture complex micro-architectured solids that achieve desired stress versus strain responses. Here, we report experimental measurements and associated finite element (FE) calculations on the effect of strut shape upon the tensile response of two-dimensional (2D) lattices made from low-carbon steel sheets. Two lattice topologies are considered: (i) a stretching-dominated triangular lattice and (ii) a bending-dominated hexagonal lattice. It is found that strut waviness can enhance the ductility of each lattice, particularly for bending-dominated hexagonal lattices. Manufacturing imperfections such as undercuts have a small effect on the ductility of the lattices but can significantly reduce the ultimate tensile strength. FE simulations provide additional insight into these observations and are used to construct design maps to aid the design of lattices with specified strength and ductility., Comment: 35 pages, 12 figures

We propose a local reinforcement technique for lattices in the vicinity of a stress-raiser such as a notch, in order to elevate the macroscopic strength and ductility. A spatially non-uniform waviness distribution of sinusoidally-shaped struts is assumed in the vicinity of the notch, and the sensitivity of macroscopic tensile response to strut waviness distribution is studied by finite element analysis. Optimized lattice structures are determined in order to maximise the macroscopic tensile strength or ductility from these various strut waviness distributions. Both hexagonal and triangular lattices are studied as these geometries are representative of bending-dominated and stretching-dominated lattices, respectively., Comment: 33 pages, 16 figures

Natural hard composites like human bone possess a combination of strength and toughness that exceeds that of their constituents and of many engineered composites. This augmentation is attributed to their complex hierarchical structure, spanning multiple length scales; in bone, characteristic dimensions range from nanoscale fibrils to microscale lamellae to mesoscale osteons and macroscale organs. The mechanical properties of bone have been studied, with the understanding that the isolated microstructure at micro- and nano-scales gives rise to superior strength compared to that of whole tissue, and the tissue possesses an amplified toughness relative to that of its nanoscale constituents. Nanoscale toughening mechanisms of bone are not adequately understood at sample dimensions that allow for isolating salient microstructural features, because of the challenge of performing fracture experiments on small-sized samples. We developed an in-situ three-point bend experimental methodology that probes site-specific fracture behavior of micron-sized specimens of hard material. Using this, we quantify crack initiation and growth toughness of human trabecular bone with sharp fatigue pre-cracks and blunt notches. Our findings indicate that bone with fatigue cracks is two times tougher than that with blunt cracks. In-situ data-correlated electron microscopy videos reveal this behavior arises from crack-bridging by nanoscale fibril structure. The results reveal a transition between fibril-bridging (~1 $\mu$m) and crack deflection/twist (~500 $\mu$m) as a function of length-scale, and quantitatively demonstrate hierarchy-induced toughening in a complex material. This versatile approach enables quantifying the relationship between toughness and microstructure in various complex material systems and provides direct insight for designing biomimetic composites.

The Large Hadron electron Collider (LHeC) is designed to move the field of deep inelastic scattering (DIS) to the energy and intensity frontier of particle physics. Exploiting energy recovery technology, it collides a novel, intense electron beam with a proton or ion beam from the High Luminosity--Large Hadron Collider (HL-LHC). The accelerator and interaction region are designed for concurrent electron-proton and proton-proton operation. This report represents an update of the Conceptual Design Report (CDR) of the LHeC, published in 2012. It comprises new results on parton structure of the proton and heavier nuclei, QCD dynamics, electroweak and top-quark physics. It is shown how the LHeC will open a new chapter of nuclear particle physics in extending the accessible kinematic range in lepton-nucleus scattering by several orders of magnitude. Due to enhanced luminosity, large energy and the cleanliness of the hadronic final states, the LHeC has a strong Higgs physics programme and its own discovery potential for new physics. Building on the 2012 CDR, the report represents a detailed updated design of the energy recovery electron linac (ERL) including new lattice, magnet, superconducting radio frequency technology and further components. Challenges of energy recovery are described and the lower energy, high current, 3-turn ERL facility, PERLE at Orsay, is presented which uses the LHeC characteristics serving as a development facility for the design and operation of the LHeC. An updated detector design is presented corresponding to the acceptance, resolution and calibration goals which arise from the Higgs and parton density function physics programmes. The paper also presents novel results on the Future Circular Collider in electron-hadron mode, FCC-eh, which utilises the same ERL technology to further extend the reach of DIS to even higher centre-of-mass energies., Comment: 373 pages, many figures, to be published by J. Phys. G

An assessment is made of the J-integral test procedure for initial crack growth in an open-cell aluminium alloy foam by combining finite element (FE) simulations with experiment. It is found experimentally that a zone of randomly failed struts develops ahead of the primary crack tip, and is comparable in size to that of the plastic zone. Hence, a crack tip J-field is absent at the initiation of crack growth from the primary crack tip. This implies that the measured J_IC value and the J versus crack extension Da curve cannot be treated as material properties despite the fact that the specimen size meets the usual criteria for J validity. The toughness tests were performed on a single-edge notched bend specimen, and crack extension was measured by the direct current potential drop method, by digital image correlation and by X-ray computed tomography. The crack growth resistance of the foam is associated with two distinct zones of plastic dissipation: (i) a bulk plastic zone emanating from the crack tip (containing a cluster of randomly failed struts), and (ii) a crack bridging zone behind the advancing crack tip. The applicability of a cohesive zone model to predict the fracture response is explored for the observed case of large scale bridging. To do so, FE simulations are performed by replacing the discrete lattice of the open-cell metallic foam by a compressible, elastic-plastic hardening solid while the fracture process zone in the foam is represented by a cohesive zone, as characterised by a tensile traction versus separation law. A detailed comparison of the cohesive zone model with experimental observations reveals that it is possible to capture the load versus displacement response but not the details of the fracture process zone using a single set of process zone parameters., Comment: 42 pages, 14 figures