A fast weakly intrusive multiscale method in explicit dynamics

This paper presents new developments on a weakly intrusive approach for the simplified implementation of space and time multiscale methods within an explicit dynamics software. The 'substitution' method proposed in previous works allows to take advantage of a global coarse model, typically used in an industrial context, running separate, refined in space and in time, local analyses only where needed. The proposed technique is iterative, but the explicit character of the method allows to perform the global computation only once per global time step, while a repeated solution is required for the small local problems only. Nevertheless, a desirable goal is to reach convergence with a reduced number of iterations. To this purpose, we propose here a new iterative algorithm based on an improved interface inertia operator. The new operator exploits a combined property of velocity Hermite time interpolation on the interface and of the central difference integration scheme, allowing the consistent upscaling of interface inertia contributions from the lower scale. This property is exploited to construct an improved mass matrix operator for the interface coupling, allowing to significantly enhance the convergence rate. The efficiency and robustness of the procedure are demonstrated through several examples of growing complexity. Copyright {\copyright} 2014 John Wiley \& Sons, Ltd

A weakly-intrusive multi-scale substitution method in explicit dynamics

For virtual testing of composite structures, the use of fine modeling seems preferable to simulate complex mechanisms like delamination. However, the associated computational costs are prohibitively high for large structures. Multi-scale coupling techniques aim at reducing such computational costs, limiting the fine model only where necessary. The dynamic adaptivity of the models represents a crucial feature to follow evolutive phenomena. Domain decomposition methods would have to be combined with re-meshing strategies, that are considered intrusive implementations within commercial software. Global-local approaches are considered less intrusive, because they allow one to use a global coarse model on the overall structure and a fine local patch eventually adapted to cover the interest zone. In our work, we developed a global-local coupling method for explicit dynamics, presented in [1] and [2] and implemented in Abaqus/Explicit via the co-simulation technique for the simulation of delamination under high velocity impact

Manipulation and Optical Detection of Colloidal Functional Plasmonic Nanostructures in Microfluidic Systems

The very strong optical resonances of plasmonic nanostructures can be harnessed for sensitive detection of chemical and biomolecular analytes in small volumes. Here we describe an approach towards optical biosensing in microfluidic systems using plasmonic structures (functionalized gold nanoparticles) in colloidal suspension. The plasmonic nanoparticles provide the optical signal, in the form of resonant light scattering or absorption, and the microfluidic environment provides means for selectively manipulating the nanoparticles through fluid dynamics and electric fields. In the first part we discuss recent literature on functionalized colloidal particles and the methods for handling them in microfluidic systems. Then we experimentally address aspects of nanoparticle functionalization, detection through plasmonic resonant light scattering under dark-field illumination and the electrokinetic behavior of the particles under the action of an alternating electric field

A partitioned model order reduction approach to rationalise computational expenses in multiscale fracture mechanics

We propose in this paper an adaptive reduced order modelling technique based on domain partitioning for parametric problems of fracture. We show that coupling domain decomposition and projection-based model order reduction permits to focus the numerical effort where it is most needed: around the zones where damage propagates. No \textit{a priori} knowledge of the damage pattern is required, the extraction of the corresponding spatial regions being based solely on algebra. The efficiency of the proposed approach is demonstrated numerically with an example relevant to engineering fracture.Comment: Submitted for publication in CMAM

A Three-Dimensional Analysis of Symmetric Composite Laminates with Damage

Damage behavior of a symmetric composite laminate without an initial im perfection or macro-crack is analyzed based on a three-dimensional lamination theory under multi-axial loading. The global response of the laminate during the damaging pro cess is determined from the individual response of its constituent plies and their mutual relations. Some specific results are presented to illustrate the damage characteristics of several typical composite laminates when they are subjected to proportional loading. The application of the method to characterize damage initiation and growth in more complex structures is also discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67341/2/10.1177_105678959300200304.pd

A three-scale domain decomposition method for the 3D analysis of debonding in laminates

The prediction of the quasi-static response of industrial laminate structures requires to use fine descriptions of the material, especially when debonding is involved. Even when modeled at the mesoscale, the computation of these structures results in very large numerical problems. In this paper, the exact mesoscale solution is sought using parallel iterative solvers. The LaTIn-based mixed domain decomposition method makes it very easy to handle the complex description of the structure; moreover the provided multiscale features enable us to deal with numerical difficulties at their natural scale; we present the various enhancements we developed to ensure the scalability of the method. An extension of the method designed to handle instabilities is also presented

Manifold learning for coherent design interpolation based on geometrical and topological descriptors

[EN] In the context of intellectual property in the manufacturing industry, know-how is referred to practical knowledge on how to accomplish a specific task. This know-how is often difficult to be synthesised in a set of rules or steps as it remains in the intuition and expertise of engineers, designers, and other professionals. Today, a new research line in this concern spot-up thanks to the explosion of Artificial Intelligence and Machine Learning algorithms and its alliance with Computational Mechanics and Optimisation tools. However, a key aspect with industrial design is the scarcity of available data, making it problematic to rely on deep-learning approaches. Assuming that the existing designs live in a manifold, in this paper, we propose a synergistic use of existing Machine Learning tools to infer a reduced manifold from the existing limited set of designs and, then, to use it to interpolate between the individuals, working as a generator basis, to create new and coherent designs. For this, a key aspect is to be able to properly interpolate in the reduced manifold, which requires a proper clustering of the individuals. From our experience, due to the scarcity of data, adding topological descriptors to geometrical ones considerably improves the quality of the clustering. Thus, a distance, mixing topology and geometry is proposed. This distance is used both, for the clustering and for the interpolation. For the interpolation, relying on optimal transport appear to be mandatory. Examples of growing complexity are proposed to illustrate the goodness of the method.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).The authors gratefully acknowledge the financial support of Ministerio de Educacion, Spain (FPU16/07121),Generalitat Valenciana, Spain (Prometeo/2021/046 and CIAICO/2021/226), Ministerio de Economia, Industriay Competitividad, Spain (DPI2017-89816-R) and FEDER. O. Allix would like to thank the French National University Council and ENS Paris-Saclay for supporting his sabbatical at UPV, which made it possible to closely interact with the colleagues from I2MB-UPV. Funding for open access charge: CRUE-Universitat Politecnica de ValenciaMuñoz-Pellicer, D.; Allix, O.; Chinesta Soria, FJ.; Ródenas, JJ.; Nadal, E. (2023). Manifold learning for coherent design interpolation based on geometrical and topological descriptors. Computer Methods in Applied Mechanics and Engineering. 405. https://doi.org/10.1016/j.cma.2022.11585940

Manifold learning for coherent design interpolation based on geometrical and topological descriptors

In the context of intellectual property in the manufacturing industry, know-how is referred to practical knowledge on how to accomplish a specific task. This know-how is often difficult to be synthesised in a set of rules or steps as it remains in the intuition and expertise of engineers, designers, and other professionals. Today, a new research line in this concern spot-up thanks to the explosion of Artificial Intelligence and Machine Learning algorithms and its alliance with Computational Mechanics and Optimisation tools. However, a key aspect with industrial design is the scarcity of available data, making it problematic to rely on deep-learning approaches. Assuming that the existing designs live in a manifold, in this paper, we propose a synergistic use of existing Machine Learning tools to infer a reduced manifold from the existing limited set of designs and, then, to use it to interpolate between the individuals, working as a generator basis, to create new and coherent designs. For this, a key aspect is to be able to properly interpolate in the reduced manifold, which requires a proper clustering of the individuals. From our experience, due to the scarcity of data, adding topological descriptors to geometrical ones considerably improves the quality of the clustering. Thus, a distance, mixing topology and geometry is proposed. This distance is used both, for the clustering and for the interpolation. For the interpolation, relying on optimal transport appear to be mandatory. Examples of growing complexity are proposed to illustrate the goodness of the method

Two new rapid SNP-typing methods for classifying Mycobacterium tuberculosis complex into the main phylogenetic lineages

There is increasing evidence that strain variation in Mycobacterium tuberculosis complex (MTBC) might influence the outcome of tuberculosis infection and disease. To assess genotype-phenotype associations, phylogenetically robust molecular markers and appropriate genotyping tools are required. Most current genotyping methods for MTBC are based on mobile or repetitive DNA elements. Because these elements are prone to convergent evolution, the corresponding genotyping techniques are suboptimal for phylogenetic studies and strain classification. By contrast, single nucleotide polymorphisms (SNP) are ideal markers for classifying MTBC into phylogenetic lineages, as they exhibit very low degrees of homoplasy. In this study, we developed two complementary SNP-based genotyping methods to classify strains into the six main human-associated lineages of MTBC, the 'Beijing' sublineage, and the clade comprising Mycobacterium bovis and Mycobacterium caprae. Phylogenetically informative SNPs were obtained from 22 MTBC whole-genome sequences. The first assay, referred to as MOL-PCR, is a ligation-dependent PCR with signal detection by fluorescent microspheres and a Luminex flow cytometer, which simultaneously interrogates eight SNPs. The second assay is based on six individual TaqMan real-time PCR assays for singleplex SNP-typing. We compared MOL-PCR and TaqMan results in two panels of clinical MTBC isolates. Both methods agreed fully when assigning 36 well-characterized strains into the main phylogenetic lineages. The sensitivity in allele-calling was 98.6% and 98.8% for MOL-PCR and TaqMan, respectively. Typing of an additional panel of 78 unknown clinical isolates revealed 99.2% and 100% sensitivity in allele-calling, respectively, and 100% agreement in lineage assignment between both methods. While MOL-PCR and TaqMan are both highly sensitive and specific, MOL-PCR is ideal for classification of isolates with no previous information, whereas TaqMan is faster for confirmation. Furthermore, both methods are rapid, flexible and comparably inexpensive