Modélisation de la compression haute densité des poudres métalliques ductiles par la méthode des éléments discrets

This Ph.D. manuscript synthesises three years of research dedicated to numerical and theoretical studies of high density powder compaction. During cold compaction process, the compaction stage is among the most sensitive powder metallurgy's stages, because it has a strong impact on the mechanical properties of the final part. It is necessary to find a numerical approach to control and to optimize the high density powder compaction (density value above 0:9). We propose to model by the discrete element method the behaviour of powder which is observed experimentally under dierent loading paths. To date, the discrete element simulations are not able to model the powder compaction for high density values (density is limited at 0:85). To go beyond this limit, we present a contact model implemented into a discrete element open-source software (Yade). This new contact model is based on a normal contact law which integrates in its expression the local density parameter. This new local variable takes into account the incompressibility of the material which appears at density values above 0:85. In order to realize more realistic simulations, a new geometric algorithm to generate polydisperse sphere packings is developed. This new numerical tool is able to generate very fast large sphere assemblies with dierent properties controlled by the user as: density distribution, the minimal and maximal size of spheres. With the contact model capable of reproducing the granular interaction up to high density value and the geometric algorithm which generates sphere assemblies similar to powder, we realize simulations of isostatic and closed die compaction for various types of powder (copper, aluminium, iron). The results are directly compared with those obtained by multi-particle finite element method and by experimental tests. These comparisons allow to validate and test the robustness of the contact model developed here. Finally, we investigate the evolution of aluminium powder assembly composed with an initial graded density distribution during the closed die compaction.Ce mémoire de thèse synthétise trois années de recherches dédiées à l'étude numérique et théorique de la compression à haute densité de poudres métalliques. Des diérentes phases qu'intègrent la métallurgie des poudres, la phase de compression à froid de la poudre est l'une des phases les plus sensibles de ce procédé de fabrication, car elle influence les propriétés mécaniques de la pièce finale. Il est donc nécessaire de mettre en place une approche numérique qui permet de contrôler et d'optimiser la compression de poudre jusqu'à de fortes valeurs de compacité (compacité supérieure à 0:9). Pour cela, nous proposons de reproduire par la méthode des éléments discrets le comportement de la poudre observé expérimentalement sous diérents types de chargement. A ce jour, les simulations via cette méthode sont limitées à une valeur de compacité ne dépassant pas 0:85. Pour dépasser ces limitations, nous présentons un modèle de contact implémenté dans un code éléments discrets libre (Yade). Ce nouveau modèle de contact est développé sur la base de la loi de contact normal qui intègre le terme de densité locale des particules dans son expression, afin de prendre en compte l'incompressibilité des grains se produisant à des valeurs de compacité supérieures à 0:85. Dans le but de procéder à des simulations plus réalistes, un nouvel algorithme géométrique de génération d'empilements de sphères polydisperses est développé. Ce nouvel outil numérique est capable de générer très rapidement de grands assemblages de sphères en contact tout en contrôlant diérents paramètres comme la distribution de la compacité, la taille minimale et maximale des sphères. Avec le modèle de contact capable de reproduire l'interaction entre les grains et la création d'un algorithme pouvant générer des assemblages de sphères similaires à un tas de poudres, nous procédons à des simulations de compression isostatique et en matrice pour diérents types de poudres (cuivre, aluminium, fer). Les résultats obtenus sont directement comparés à ceux issus des simulations éléments finis multi-particules et de l'expérience. Ces comparaisons permettent ainsi de valider et de tester la robustesse du modèle de contact développé. Pour finir, nous investiguons sur la base de nos divers développements validés, l'évolution d'une poudre d'aluminium avec un gradient de compacité au cours d'une compression en matrice

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.Funding: the Tri-I Program in Computational Biology and Medicine (CBM) funded by NIH grant 1T32GM083937; GitHub; Philip Blood and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1548562 and NSF award number ACI-1445606; NASA (NNX14AH50G, NNX17AB26G), the NIH (R01AI151059, R25EB020393, R21AI129851, R35GM138152, U01DA053941); STARR Foundation (I13- 0052); LLS (MCL7001-18, LLS 9238-16, LLS-MCL7001-18); the NSF (1840275); the Bill and Melinda Gates Foundation (OPP1151054); the Alfred P. Sloan Foundation (G-2015-13964); Swiss National Science Foundation grant number 407540_167331; NIH award number UL1TR000457; the US Department of Energy Joint Genome Institute under contract number DE-AC02-05CH11231; the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy; Stockholm Health Authority grant SLL 20160933; the Institut Pasteur Korea; an NRF Korea grant (NRF-2014K1A4A7A01074645, 2017M3A9G6068246); the CONICYT Fondecyt Iniciación grants 11140666 and 11160905; Keio University Funds for Individual Research; funds from the Yamagata prefectural government and the city of Tsuruoka; JSPS KAKENHI grant number 20K10436; the bilateral AT-UA collaboration fund (WTZ:UA 02/2019; Ministry of Education and Science of Ukraine, UA:M/84-2019, M/126-2020); Kyiv Academic Univeristy; Ministry of Education and Science of Ukraine project numbers 0118U100290 and 0120U101734; Centro de Excelencia Severo Ochoa 2013–2017; the CERCA Programme / Generalitat de Catalunya; the CRG-Novartis-Africa mobility program 2016; research funds from National Cheng Kung University and the Ministry of Science and Technology; Taiwan (MOST grant number 106-2321-B-006-016); we thank all the volunteers who made sampling NYC possible, Minciencias (project no. 639677758300), CNPq (EDN - 309973/2015-5), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, ECNU, the Research Grants Council of Hong Kong through project 11215017, National Key RD Project of China (2018YFE0201603), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) (L.S.