Laboratoire Modélisation et Simulation Multi-Echelle

Abstract The work reported in “Granular micromechanics‐based identification of isotropic strain gradient parameters for elastic geometrically nonlinear deformations” misidentified key terms in the grain‐pair objective relative displacement when accounting for the second gradient of placement. In this paper, we correct that oversight by deriving a revised expression for the grain‐pair objective relative displacement within the granular micromechanics framework. The amended terms, which resemble Christoffel symbols expressed in terms of strain gradients, modify the contributions of both the normal and tangential components to the strain energy and, consequently, alter the identified strain‐gradient elastic parameters. Importantly, the identification of the standard (first gradient) elastic tensor remains unchanged. This brief paper presents the corrected derivation, the resulting stiffness tensors for anisotropic strain‐gradient elasticity, and updated analytical expressions for the material parameters in both 2D and 3D isotropic settings.

In this paper, we propose an efficient FFT (Fast Fourier Transform) based numerical method to determine the acoustical properties of sound absorbing porous materials, including dynamic viscous and thermal permeability, from the pixelized/voxelized microstructure. At the local scale, the governing equations of the two problems are periodic and dynamic generalizations of Stokes and Laplace equations with inertial body terms depending on the excitation frequency ω . To solve the problems in regular grids, the discrete frequency dependent Green’s functions of the two problems in Fourier space are obtained using finite difference approximation and used to construct boundary equations for the distribution of force and source terms. The equations can then be solved by iterative solvers for the two cases ω = 0 and ω ≠ 0 , respectively. Numerical tests for different pixelized and voxelized porous microstructure confirm the good performance and accuracy of the method. • Computing sound absorbing properties of porous materials by FFT based approach. • Solving dynamic Stokes and Laplace problems in periodic microstructure. • Discrete Green’s function in Fourier space using finite difference approximation. • Matrix-free implementation enhances computation speed and storage efficiency. • Use of voxelized microstructures, suitable for tomography data.

High tibial osteotomy is a common procedure for knee osteoarthritis during which the surgeon partially opens the tibia and must stop impacting when cortical bone is reached by the osteotome. Surgeons rely on their proprioception and fluoroscopy to conduct the surgery. Our group has developed an instrumented hammer to assess the mechanical properties of the material surrounding the osteotome tip. The aim of this ex vivo study is to determine whether this hammer can be used to detect the transition from cortical to trabecular bone and vice versa. Osteotomies were performed until rupture in pig tibia using the instrumented hammer. An algorithm was developed to detect both transitions based on the relative variation of an indicator derived from the time variation of the force. The detection by the algorithm of both transitions was compared with the position of the osteotome measured with a video camera and with surgeon proprioception. The difference between the detection of the video and the algorithm (respectively, the video and the surgeon; the surgeon and the algorithm) is 1.0$\pm$1.5 impacts (respectively, 0.5$\pm$0.6 impacts; 1.4$\pm$1.8 impacts), for the detection of the transition from the cortical to trabecular bone. For the transition from the trabecular to cortical bone, the difference is 3.6$\pm$2.6 impacts (respectively, 3.9$\pm$2.4 impacts; 0.8$\pm$0.9 impacts), and the detection by the algorithm was always done before the sample rupture. This ex vivo study demonstrates that this method could prevent impacts leading to hinge rupture.

Moisture-based power generation technology captures energy from moisture and can directly power small electronic devices. Effectively utilizing atmospheric water at a wide humidity range and converting it into electrical output at the same level is valuable. However, most devices significantly differ in power generation performance between high and low humidity environments, so the dynamic adaptability of wide humidity remains a major challenge for existing moisture-based power generators. Here, a biomimetic intelligent nanofluid diode is designed with a polypyrrole (PPy)/anodized aluminum oxide (AAO)/poly (diallyldimethylammonium chloride) (PDDA) sandwich structure, which can dynamically adjust the surface charge and pore size of the nanochannel according to external humidity, achieving dynamic adaptive regulation of moisture capture and ion rectification transport behavior. The ability of moisture capturing and the rectification effect both increase with decreasing humidity. Thus, the number and migration efficiency of free charge carriers are improved under low humidity conditions, and the maximum difference in output power remains within 40% (26.3-44 mW m-2) in a wide humidity range (15% -93% RH). The humidity-adaptive properties of membranes enable devices to operate over a wide range of moisture, providing a new approach to enhance the adaptability of nanogenerators to complex environments.

Most existing research on the identification of moving loads in the presence of uncertainties primarily focuses on parametric uncertainties related to the variability or the lack of knowledge of some parameters of the computational model. Such an approach does not allow the consideration of uncertainties related to modelling errors, for instance those related to the discretisation of the structure using the Finite Element (FE) method, simplifying assumptions when deriving the constituent equations, and the idealisation of the boundary conditions. In this paper, a methodology that investigates moving load identification in presence of model-form uncertainties using a non-parametric approach is established. A substructuring-like approach is used to reduce the computational model complexity and to separate the supporting structure from its boundaries, enabling independent control over uncertainty levels within each component of the computational model. A non-parametric probabilistic approach is introduced to the inner part of the reduced-order computational model, while a two-level probabilistic approach is applied to model the uncertainties in the boundaries of the supporting structure. This formulation offers several advantages, notably reducing computational time and accommodating and controlling various types of uncertainties. The efficiency and applicability of this approach are demonstrated through several numerical examples. • A substructuring approach is used to separate the inner part from its boundary. • A general random spring model is introduced to model uncertainties in the boundary. • A generalised- α integration scheme is used to identify the stochastic moving loads. • Statistics of the moving load are calculated using the Monte Carlo method.

Seismic vulnerability assessment is crucial for ensuring the structural safety of buildings, particularly in earthquake-prone regions. While Nonlinear Time History Analysis (NLTHA) provides high accuracy, its computational demands make it impractical for rapid assessments. Machine learning (ML) models, especially Artificial Neural Networks (ANN), offer an efficient alternative but often require large datasets for reliable predictions. This study introduces a hybrid Principal Component Analysis-Artificial Neural Network (PCA-ANN) model to enhance seismic response prediction by reducing input dimensionality while preserving critical information. A dataset of over one million seismic responses was generated using NLTHA on three reinforced concrete (RC) frame buildings subjected to various ground motions. Comparative analysis between PCA-ANN and conventional ANN models reveals that PCA-ANN significantly improves both predictive accuracy and computational efficiency. The PCA-ANN model achieved a correlation coefficient (R 2 ) of 99.1% and reduced Mean Squared Error (MSE) by 87% compared to the standalone ANN. Additionally, PCA-ANN maintained robust performance with limited dataset sizes, achieving an R 2 above 75% using only 25% of the dataset, whereas ANN failed under similar conditions. Further validation through Incremental Dynamic Analysis (IDA) and fragility curves shows that PCA-ANN exhibits discrepancies below 2% compared to NLTHA. The model also achieves the lowest Relative Squared Error (RSR) (18%, 21%, and 24% for low-, mid-, and high-rise buildings, respectively) and the lowest Percentage Bias (PBias) (1.7%, 0.5%, and 0.3% for the same building types) when utilizing the full dataset. These results highlight PCA-ANN’s superior reliability across varying structural heights and dataset sizes. This study demonstrates that PCA-ANN is an efficient and accurate tool for seismic risk assessment, reducing computational costs while maintaining predictive reliability.

ABSTRACTA framework is proposed to bridge coarse and fine meshes in a single simulation within the phase field method for fracture. Fine meshes are used in the vicinity of localized defects to accurately capture crack initiation, while coarse meshes are used away from initial defects and include only crack propagation paths. This reduces the prohibitive computational times associated with uniformly fine meshes over the entire domain, or with the use of complex adaptive meshes in the phase field method. The coupling between the two overlapping meshes is achieved using a variational formulation in which the energies of the models associated with the fine and coarse meshes are weighted in the superposition zone. Two situations are considered. The first includes the resolution of the phase field problem only in the fine mesh, while the coarse mesh is limited to the undamaged elastic problem. In the second situation, cracks can propagate in both the fine and coarse mesh. Variational formulations and associated finite element implementations are detailed. Numerical examples are presented, showing the potential of this approach to significantly reduce computational costs in the phase field method for cracking without affecting the accuracy.

Thin low-frequency acoustic absorbers that are economical to produce using a large-scale manufacturing process are scarce, and their efficiency is often limited to a narrow frequency range. In this paper, perforated gypsum foams are shown to achieve high absorption levels for layers thinner than 1/15 of a wavelength. With a mass density of 150 kg/m 3 , they are as practical in use as conventional porous absorbers. To reach acoustic absorption levels higher than 0.7, perforations with a diameter smaller than 1 mm open a fraction of the initially closed pores. To allow additional design freedom, the foams' pore size can be varied between 1 and 3 mm, while keeping the wall thickness as low as 0.1 mm. Simulations of the microscopic fluid flow in a representative volume element show how the combination of foam properties and perforation patterns can be combined such that sub-wavelength absorption is obtained. Comparing the predicted sound absorption with impedance tube measurements demonstrates that the solution can yield a wideband low-frequency sound absorption peak. The most remarkable example exhibits an absorption peak higher than 0.7 between 525 Hz and 875 Hz for a layer of 25 mm, a result equivalent to 1/21 of the incident sound wavelength. • Lightweight, highly porous, mineral foams are perforated to increase and tune their sound absorption properties. • The manufacturing process ensures reproducible foam properties (porosity, pore size). • Microscopic flow simulations predict the acoustic properties of the perforated foams. • The absorption spectrum is designed to achieve requirements, by changing pore and perforation sizes. • A sub-wavelength sound absorber, thinner than 1/15 of the acoustic incident wavelength, is possible.