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How functional amyloids are regulated to restrict their activity is poorly understood. The cytoplasmic polyadenylation element-binding protein 3 (CPEB3) is an RNA-binding protein that adopts an amyloid state key for memory persistence. Its monomer represses the translation of synaptic target mRNAs while phase separated, whereas its aggregated state acts as a translational activator. Here, we have explored the sequence-driven molecular determinants behind the functional aggregation of human CPEB3 (hCPEB3). We found that the intrinsically disordered region (IDR) of hCPEB3 encodes both an amyloidogenic and a phase separation domain, separated by a poly-A-rich region. The hCPEB3 amyloid core is composed by a hydrophobic region instead of the Q-rich stretch found in the Drosophila orthologue. The hCPEB3 phase separation domain relies on hydrophobic interactions with ionic strength dependence, and its droplet ageing process leads to a liquid-to-solid transition with the formation of a non-fibril-based hydrogel surrounded by starburst droplets. Furthermore, we demonstrate the differential behavior of the protein depending on its environment. Under physiological-like conditions, hCPEB3 can establish additional electrostatic interactions with ions, increasing the stability of its liquid droplets and driving a condensation-based amyloid pathway.

Impact simulations for damage resistance analysis are computationally intensive due to contact algorithms and advanced damage models. Both methods, which are the main ingredients in an impact event, require refined meshes at the contact zone to obtain accurate predictions of the contact force and damage onset and propagation through the material. This work presents the application of the partial Dirichlet–Neumann contact algorithm to simulate low-velocity impact problems on composite structures using High-Performance Computing. This algorithm is devised for parallel finite element codes running on supercomputers, and it is extended to explicit time integration schemes to solve impact problems including damage. The proposed framework is validated with a standard test for damage resistance on fiber-reinforced polymer matrix composites. Moreover, the parallel performance of the proposed algorithm has been evaluated in a mesh of 74M of elements running with 2400 processors, This work has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreements No. 807083 and No. 945521 (SHERLOC project). The JU receives support from the European Union’s Horizon 2020 research and innovation program and the Clean Sky 2 JU members other than the Union. The authors gratefully acknowledge Hellenic Aerospace Industry for manufacturing of the coupons made of T800S/M21 material and Kirsa Muñoz and Miguel Ángel Jiménez from Element Materials Technology Seville, Peer Reviewed, Postprint (author's final draft)

[EN] Cardiovascular diseases currently have a major social and economic impact, constituting one of the leading causes of mortality and morbidity. Personalized computational models of the heart are demonstrating their usefulness both to help understand the mechanisms underlying cardiac disease, and to optimize their treatment and predict the patient's response. Within this framework, the Spanish Research Network for Cardiac Computational Modelling (VHeart-SN) has been launched. The general objective of the VHeart-SN network is the development of an integrated, modular and multiscale multiphysical computational model of the heart. This general objective is addressed through the following specific objectives: a) to integrate the different numerical methods and models taking into account the specificity of patients; b) to assist in advancing knowledge of the mechanisms associated with cardiac and vascular diseases; and c) to support the application of different personalized therapies. This article presents the current state of cardiac computational modelling and different scientific works conducted by the members of the network to gain greater understanding of the characteristics and usefulness of these models., [ES] Las enfermedades cardiovasculares tienen en la actualidad un gran impacto social y económico y constituyen una de las principales causas de mortalidad y morbilidad. Los modelos computacionales personalizados del corazón están demostrando ser útiles tanto para ayudar a comprender los mecanismos subyacentes a las patologías cardiacas como para optimizar su tratamiento prediciendo la respuesta del paciente. En este contexto, se ha puesto en marcha la Red Española de Investigación en Modelización Computacional Cardiaca (V-Heart SN). El objetivo general de V-Heart SN es el desarrollo de un modelo computacional multifísico y multiescala integrado del corazón. Este objetivo general se aborda a través de los siguientes objetivos específicos: a) integrar los diferentes modelos numéricos teniendo en cuenta la especificidad de los pacientes; b) ayudar a avanzar en el conocimiento de los mecanismos asociados a las diferentes patologías cardiacas y vasculares; y c) apoyar la aplicación de terapias personalizadas. Este artículo presenta el estado actual de la modelización computacional cardiaca y los diferentes trabajos científicos desarrollados por los miembros de la red para favorecer una mayor comprensión de las características y utilidad de los modelos.

Cellulose is the most abundant organic molecule on Earth and represents a renewable and practically everlasting feedstock for the production of biofuels and chemicals. Self-assembled owing to the high-affinity cohesin-dockerin interaction, cellulosomes are huge multi-enzyme complexes with unmatched efficiency in the degradation of recalcitrant lignocellulosic substrates. The recruitment of diverse dockerin-borne enzymes into a multicohesin protein scaffold dictates the three-dimensional layout of the complex, and interestingly two alternative binding modes have been proposed. Using single-molecule fluorescence resonance energy transfer and molecular simulations on a range of cohesin-dockerin pairs, we directly detect varying distributions between these binding modes that follow a built-in cohesin-dockerin code. Surprisingly, we uncover a prolyl isomerase-modulated allosteric control mechanism, mediated by the isomerization state of a single proline residue, which regulates the distribution and kinetics of binding modes. Overall, our data provide a novel mechanistic understanding of the structural plasticity and dynamics of cellulosomes.

Las enfermedades cardiovasculares tienen en la actualidad un gran impacto social y económico y constituyen una de las principales causas de mortalidad y morbilidad. Los modelos computacionales personalizados del corazón están demostrando ser útiles tanto para ayudar a comprender los mecanismos subyacentes a las patologías cardiacas como para optimizar su tratamiento prediciendo la respuesta del paciente. En este contexto, se ha puesto en marcha la Red Española de Investigación en Modelización Computacional Cardiaca (V-Heart SN). El objetivo general de V-Heart SN es el desarrollo de un modelo computacional multifísico y multiescala integrado del corazón. Este objetivo general se aborda a través de los siguientes objetivos específicos: a) integrar los diferentes modelos numéricos teniendo en cuenta la especificidad de los pacientes; b) ayudar a avanzar en el conocimiento de los mecanismos asociados a las diferentes patologías cardiacas y vasculares; y c) apoyar la aplicación de terapias personalizadas. Este artículo presenta el estado actual de la modelización computacional cardiaca y los diferentes trabajos científicos desarrollados por los miembros de la red para favorecer una mayor comprensión de las características y utilidad de los modelos., Postprint (author's final draft)

In this work we present a robust interface coupling algorithm called Compact Interface quasi-Newton (CIQN). It is designed for computationally intensive applications using an MPI multi-code partitioned scheme. The algorithm allows to reuse information from previous time steps, feature that has been previously proposed to accelerate convergence. Through algebraic manipulation, an efficient usage of the computational resources is achieved by: avoiding construction of dense matrices and reduce every multiplication to a matrix–vector product and reusing the computationally expensive loops. This leads to a compact version of the original quasi-Newton algorithm. Altogether with an efficient communication, in this paper we show an efficient scalability up to 4800 cores. Three examples with qualitatively different dynamics are shown to prove that the algorithm can efficiently deal with added mass instability and two-field coupled problems. We also show how reusing histories and filtering does not necessarily makes a more robust scheme and, finally, we prove the necessity of this HPC version of the algorithm. The novelty of this article lies in the HPC focused implementation of the algorithm, detailing how to fuse and combine the composing blocks to obtain an scalable MPI implementation. Such an implementation is mandatory in large scale cases, for which the contact surface cannot be stored in a single computational node, or the number of contact nodes is not negligible compared with the size of the domain., This work has been funded by CompBioMed project a grant by the EuropeanCommission H2020 (agreement nr: 823712), EUBrazilCC a project under the Programme FP7-ICT (agreement nr: 614048) and a FPI-SO grant (agreementnr: SVP-2014-068491, Peer Reviewed, Postprint (author's final draft)

High fidelity Computational Fluid Dynamics simulations are generally associated with large computing requirements, which are progressively acute with each new generation of supercomputers. However, significant research efforts are required to unlock the computing power of leading-edge systems, currently referred to as pre-Exascale systems, based on increasingly complex architectures. In this paper, we present the approach implemented in the computational mechanics code Alya. We describe in detail the parallelization strategy implemented to fully exploit the different levels of parallelism, together with a novel co-execution method for the efficient utilization of heterogeneous CPU/GPU architectures. The latter is based on a multi-code co-execution approach with a dynamic load balancing mechanism. The assessment of the performance of all the proposed strategies has been carried out for airplane simulations on the POWER9 architecture accelerated with NVIDIA Volta V100 GPUs., This work is partially supported by the BSC-IBM Deep Learning Research Agreement, under JSA \Application porting, analysis and optimization for POWER and POWER AI". It has also been partially supported by the EX-CELLERAT project funded by the European Commission's ICT activity of the H2020 Programme under grant agreement number: 823691. It has also received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number: 846139 (Exa-FireFlows). This paper expresses the opinions of the authors and not necessarily those of the European Commission. The European Commission is not liable for any use that may be made of the information contained in this paper. This work has also been nan- cially supported by the Ministerio de Economia, Industria y Competitividad, of Spain (TRA2017-88508-R). The computing experiments of this paper have been performed on the resources of the Barcelona Supercomputing Center., Peer Reviewed, Postprint (author's final draft)

Larger supercomputers allow the simulation of more complex phenomena with increased accuracy. Eventually this requires finer and thus also larger geometric discretizations. In this context, and extrapolating to the Exascale paradigm, meshing operations such as generation, deformation, adaptation/regeneration or partition/load balance, become a critical issue within the simulation workflow. In this paper we focus on mesh partitioning. In particular, we present a fast and scalable geometric partitioner based on Space Filling Curves (SFC), as an alternative to the standard graph partitioning approach. We have avoided any computing or memory bottleneck in the algorithm, while we have imposed that the solution achieved is independent (discounting rounding off errors) of the number of parallel processes used to compute it. The performance of the SFC-based partitioner presented has been demonstrated using up to 4096 CPU-cores in the Blue Waters supercomputer., The research leading to these results has received funding from the European Union Horizon 2020 Programme (2014-2020) and the Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP) under the HPC4E Project (grant agreement No. 689772). This work is also part of the PRAC "Simulations of Aircraft Engines" supported by the National Science Foundation. It has also been financially supported by the PRACE preparatory access projects funded in part by the EU Horizon 2020 research and innovation programme (2014-2020) under grant agreement 653838. J.C. Cajas acknowledges the nancial sup- port of the `Consejo Nacional de Ciencia y Tecnolog a (CONACyT, M exico)' grant number 231588 290790. Ricard Borrell and Daniel Mira acknowledge the Juan de la Cierva postdoctoral grants with codes IJCI-2014-21034 and IJCI-2015-26686, respectively., Postprint (author's final draft)

Validated computer simulations of the airflow and particle dynamics in human nasal cavities are important for local, segmental and total deposition predictions of both inhaled toxic and therapeutic particles. Considering three, quite different subject-specific nasal airway configurations, micron-particle transport and deposition for low-to-medium flow rates have been analyzed. Of special interest was the olfactory region from which deposited drugs could readily migrate to the central nervous system for effective treatment. A secondary objective was the development of a new dimensionless group with which total particle deposition efficiency curves are very similar for all airway models, i.e., greatly reducing the impact of intersubject variability. Assuming dilute particle suspensions with inhalation flow rates ranging from 7.5 to 20 L/min, the airflow and particle-trajectory equations were solved in parallel with the in-house, multi-purpose Alya program at the Barcelona Supercomputing Center. The geometrically complex nasal airways generated intriguing airflow fields where the three subject models exhibit among them both similar as well as diverse flow structures and wall shear stress distributions, all related to the coupled particle transport and deposition. Nevertheless, with the new Stokes-Reynolds-number group, , the total deposition-efficiency curves for all three subjects and flow rates almost collapsed to a single function. However, local particle deposition efficiencies differed significantly for the three subjects when using particle diameters = 2, 10, and . Only one of the three subject-specific olfactory regions received, at relatively high values of the inertial parameter , some inhaled microspheres. Clearly, for drug delivery to the brain via the olfactory region, a new method of directional inhalation of nanoparticles would have to be implemented., The authors acknowledge Dr. Rick Corley and colleagues at Pacific Northwest National Laboratory for providing the subject B nasal surface geometry and Dr. Edgar Matida and Dr. Matthew Johnson at Carleton University for providing the subject C nasal surface geometry, Peer Reviewed, Postprint (published version)

Bacterial cellulases are drawing increased attention as a means to obtain plentiful chemical feedstocks and fuels from renewable lignocellulosic biomass sources. Certain bacteria deploy a large extracellular multi-protein complex, called the cellulosome, to degrade cellulose. Scaffoldin, a key non-catalytic cellulosome component, is a large protein containing a cellulose-specific carbohydrate-binding module and several cohesin modules which bind and organize the hydrolytic enzymes. Despite the importance of the structure and protein/protein interactions of the cohesin module in the cellulosome, its structure in solution has remained unknown to date. Here, we report the backbone 1H, 13C and 15N NMR assignments of the Cohesin module 5 from the highly stable and active cellulosome from Clostridium thermocellum. These data reveal that this module adopts a tightly packed, well folded and rigid structure in solution. Furthermore, since in scaffoldin, the cohesin modules are connected by linkers we have also characterized the conformation of a representative linker segment using NMR spectroscopy. Analysis of its chemical shift values revealed that this linker is rather stiff and tends to adopt extended conformations. This suggests that the scaffoldin linkers act to minimize interactions between cohesin modules. These results pave the way towards solution studies on cohesin/dockerin's fascinating dual-binding mode.

The Sparse Matrix Vector Product (SpMV) is one of the main operations of iterative solvers, and, in a parallel context, it is also the siege of point-to-point communications between the neighboring MPI processes. The parallel SpMV is built in such a way that it gives, up to round off errors, the same result as its sequential counterpart. In this regards, nodes on the interfaces (or halo nodes if halo are considered) are duplicated nodes of the same original mesh. It is therefore limited to matching meshes. In this work, we generalize the parallel SpMV to glue the solution of non-matching (non-conforming) meshes through the introduction of transmission matrices. This extension of the SpMV thus enables the implicit and parallel solution of partial differential equations on non-matching meshes, as well as the implicit coupling of multiphysics problems, such as fluid-structure interactions. The proposed method is developed similarly to classical parallelization techniques and can therefore be implemented by modifying few subroutines of an already MPI-based code. According to the proposed framework, the classical parallelization technique appears as a particular case of this general setup., Peer Reviewed, Postprint (author's final draft)

The presented work addresses the investigation of the heat loss of a confined turbulent jet flame in a lab-scale combustor using a conjugate-heat transfer approach and large-eddy simulation. The analysis includes the assessment of the principal mechanisms of heat transfer in this combustion chamber: radiation, convection and conduction of heat over walls. A staggered approach is used to couple the reactive flow field to the heat conduction through the solid and both domains are solved using two implementations of the same code. Numerical results are compared against experimental data and an assessment of thermal boundary conditions to improve the prediction of the reactive flow field is given., The research leading to these results has received funding through the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7, 2007–2013) under the Grant agreement No. FP7-290042 for the project COPA-GT as well as the European Union’s Horizon 2020 Programme (2014–2020) and from Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP) under the HPC4E Project, Grant agreement No. 689772. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the Red Española de Supercomputación (RES). Finally, the authors would like to thank O. Lammel for the useful discussions and kindly providing the data for the comparison., Peer Reviewed, Postprint (published version)

Large scale time-dependent particle simulations can generate massive amounts of data, making it so that storing the results is often the slowest phase and the primary time bottleneck of the simulation. Furthermore, analysing this amount of data with traditional tools has become increasingly challenging, and it is often virtually impossible to have a visual representation of the full set. We propose a novel architecture that integrates an HPC-based multi-physics simulation code, a NoSQL database, and a data analysis and visualisation application. The goals are two: On the one hand, we aim to speed up the simulations taking advantage of the scalability of key-value data stores, while at the same time enabling real-time approximated data visualisation and interactive exploration. On the other hand, we want to make it efficient to explore and analyse the large data base of results produced. Therefore, this work represents a clear example of integrating High Performance Computing with High Performance Data Analytics. Our prototype proves the validity of our approach and shows great performance improvements. Indeed, we reduced by 67.5% the time to store the simulation while we made real-time queries run 52 times faster than alternative solutions., This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 720270 (HBP SGA1). It is also partially supported by the grant SEV-2011-00067 of Severo Ochoa Program, the TIN2015-65316-P project, with funding from the Spanish Ministry of Economy and Competitivity, the European Union FEDER funds, and the SGR 2014-SGR-1051., Peer Reviewed, Postprint (published version)

Alya is a multi-physics simulation code developed at Barcelona Supercomputing Center (BSC). From its inception Alya code is designed using advanced High Performance Computing programming techniques to solve coupled problems on supercomputers efficiently. The target domain is engineering, with all its particular features: complex geometries and unstructured meshes, coupled multi-physics with exotic coupling schemes and physical models, ill-posed problems, flexibility needs for rapidly including new models, etc. Since its beginnings in 2004, Alya has scaled well in an increasing number of processors when solving single-physics problems such as fluid mechanics, solid mechanics, acoustics, etc. Over time, we have made a concerted effort to maintain and even improve scalability for multi-physics problems. This poses challenges on multiple fronts, including: numerical models, parallel implementation, physical coupling models, algorithms and solution schemes, meshing process, etc. In this paper, we introduce Alya's main features and focus particularly on its solvers. We present Alya's performance up to 100.000 processors in Blue Waters, the NCSA supercomputer with selected multi-physics tests that are representative of the engineering world. The tests are incompressible flow in a human respiratory system, low Mach combustion problem in a kiln furnace, and coupled electro-mechanical contraction of the heart. We show scalability plots for all cases and discuss all aspects of such simulations, including solver convergence., Peer Reviewed, Postprint (published version)

This paper introduces a preconditioned variational multiscale stabilization (P-VMS) method for compressible flows. In this introductory paper we focus on inviscid flow and steady state problems. The Euler equations are solved on fully unstructured grids and discretized using the finite element method. The P-VMS method can be decomposed in three parts. First, a local preconditioner is applied to the continuous equations to reduce the stiffness while covering a wide range of Mach numbers. Then, the resulting preconditioned system is discretized in space using finite elements and stabilized with a variational multiscale stabilization method adapted for the preconditioned equations. In this paper, the solution is advanced in time using a fully explicit time discretization, although P-VMS is general and can be applied to fully implicit solvers. The proposed method is assessed by comparing convergence and accuracy of the solutions between the non-preconditioned and preconditioned cases, in particular for van Leer-Lee-Roe’s and Choi-Merkle’s preconditioners, in some selected examples covering a large range of Mach numbers., Peer Reviewed, Postprint (author's final draft)

Alya is a multi-physics simulation code developed at Barcelona Supercomputing Center (BSC). From its inception Alya code is designed using advanced High Performance Computing programming techniques to solve coupled problems on supercomputers efficiently. The target domain is engineering, with all its particular features: complex geometries and unstructured meshes, coupled multi-physics with exotic coupling schemes and physical models, ill-posed problems, flexibility needs for rapidly including new models, etc. Since its beginnings in 2004, Alya has scaled well in an increasing number of processors when solving single-physics problems such as fluid mechanics, solid mechanics, acoustics, etc. Over time, we have made a concerted effort to maintain and even improve scalability for multi-physics problems. This poses challenges on multiple fronts, including: numerical models, parallel implementation, physical coupling models, algorithms and solution schemes, meshing process, etc. In this paper, we introduce Alya's main features and focus particularly on its solvers. We present Alya's performance up to 100.000 processors in Blue Waters, the NCSA supercomputer with selected multi-physics tests that are representative of the engineering world. The tests are incompressible flow in a human respiratory system, low Mach combustion problem in a kiln furnace, and coupled electro-mechanical contraction of the heart. We show scalability plots for all cases and discuss all aspects of such simulations, including solver convergence., The authors would like to thank the following fellow researchers and institutions: • The Private Sector Program at NCSA and the BlueWaters sustained-petascale computing project-supported by the National Science Foundation (award number OCI 07-25070) and the state of Illinois. • Denis Doorly and Alister Bates (Imperial College London, UK), collaborators of the airways study. Part of this work was financed by European PRACE Type B/C projects. • The heart geometry was provided by Dr. A. Berruezo (Hospital Clinic de Barcelona) in collaboration with R. Sebastian (UVEG) and O. Camara (UPF), partially financed through project TIN2011-28067 from MINECO, Spain. • Part of the cardiac model development was financed by the grant SEV-2011-00067 of Severo Ochoa Program, awarded by the Spanish Government. • Part of the kiln model development was financed by the European Commission in the framework of the FP7 Collaborative project “Advanced Technologies for the Production of Cement and Clean Aggregates from Construction and Demolition Waste (C2CA)”, Grant Agreement No 265189., Peer Reviewed, Postprint (author's final draft)

The dynamics of unsteady flow in the human large airways during a rapid inhalation were investigated using highly detailed large-scale computational fluid dynamics on a subject-specific geometry. The simulations were performed to resolve all the spatial and temporal scales of the flow, thanks to the use of massive computational resources. A highly parallel finite element code was used, running on two supercomputers, solving the transient incompressible Navier–Stokes equations on unstructured meshes. Given that the finest mesh contained 350 million elements, the study sets a precedent for large-scale simulations of the respiratory system, proposing an analysis strategy for mean flow, fluctuations and wall shear stresses on a rapid and short inhalation (a so-called sniff). The geometry used encompasses the exterior face and the airways from the nasal cavity, through the trachea and up to the third lung bifurcation; it was derived from a contrast-enhanced computed tomography (CT) scan of a 48-year-old male. The transient inflow produces complex flows over a wide range of Reynolds numbers (Re). Thanks to the high fidelity simulations, many features involving the flow transition were observed, with the level of turbulence clearly higher in the throat than in the nose. Spectral analysis revealed turbulent characteristics persisting downstream of the glottis, and were captured even with a medium mesh resolution. However a fine mesh resolution was found necessary in the nasal cavity to observe transitional features. This work indicates the potential of large-scale simulations to further understanding of airway physiological mechanics, which is essential to guide clinical diagnosis; better understanding of the flow also has implications for the design of interventions such as aerosol drug delivery., We acknowledge PRACE for awarding us access to resource FERMI based in Italy at Bologna hosted by Cineca. This work was financially supported by the PRACE project Pra04 693 (2011050693 to the Fourth PRACE regular call). The second author gratefully acknowledges support from project ‘MatComPhys’ under the European Research Executive Agency FP7-PEOPLE-2011- IEF framework. The third author was supported by the Engineering and Physical Sciences Research Council [grant number EP/ M506345/1]., Peer Reviewed, Postprint (author's final draft)

The present work addresses the coupling of a flamelet database, to a low-Mach approximation of the Navier–Stokes equations using scalar controlling variables. The model is characterized by the chemistry tabulation based on laminar premixed flamelets in combination with an optimal choice of the reaction progress variable, which is determined based on the computational singular perturbation (CSP) method. The formulation of the model focuses on turbulent premixed flames taking into account the effect of heat losses, but it is easily extended to partially premixed and non-premixed regimes. The model is designed for applications in both, Reynolds-averaged Navier–Stokes (RANS) as well as large-eddy simulations (LES) and results for the two methods are compared. A priori analysis of the database is presented to demonstrate the influence of the reaction progress definition and the chemistry tabulation is validated against a one-dimensional premixed laminar flame. The validation of the turbulent case is performed using a tur- bulent premixed confined jet flame subject to strong heat losses, in which the model shows a good overall performance., The research leading to these results has received funding through the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7, 2007– 2013) under the grant agreement No. FP7-290042 for the project COPA-GT. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the Red Española de Supercomputación (RES). Finally, the authors would like to thank O. Lammel for kindly providing the data for comparison., Peer Reviewed, Postprint (author's final draft)

The implementations of an updated body-fitted and a non body-fitted method to deal with the interaction of a fluid and a rigid body are described. The physics of the fluid is modeled by the incompressible Navier-Stokes equations. A parallel fluid solver based on the VMS (Variational Multiscale) Finite Element method serves as the basis for the implementation. For the rigid body movement, the Newton-Euler equations are solved numerically. To account for the interaction, the force that the fluid exerts on the rigid body is determined, on the one hand. On the other hand, the velocity of the rigid body is imposed as a Dirichlet boundary condition on the fluid. A fixed Eulerian mesh discretizes the fluid domain, except for nodes in the vicinity of the rigid body boundary for the case of the updated body-fitted approach. The wet boundary of the rigid body is embedded in the fluid mesh and tracked by a moving surface mesh. It is a distinctive characteristic of the updated body-fitted strategy that, in order to impose velocities on the interface, some of the nodes near the body surface are moved by using a local r-adaptivity algorithm to conform with this surface. By contrast, the non body-fitted approach uses kriging interpolation for velocity prescription over the fluid on the interface. Given that fluid nodes can become solid nodes and viceversa due to the rigid body movement, we have adopted the FMALE approach, a variation of the ALE method to keep the fluid mesh fixed. Algorithms to ensure high performance, like skd-trees to determine if a given spatial point is currently inside the solid, are also used. All these ingredients constitute two approaches that are both computationally efficient and accurate. Numerical experiments are presented to assess their performance comparatively., We would like to especially thank Oscar Peredo for his idea and support in introducing a more technical notation and terminology for the data-structures described in this work., Peer Reviewed, Postprint (author's final draft)

Because of their widely generalized use in many industries, composites are the subject of many research campaigns. More particularly, the development of both accurate and flexible numerical models able to capture their intrinsically multiscale modes of failure is still a challenge. The standard finite element method typically requires intensive remeshing to adequately capture the geometry of the cracks and high accuracy is thus often sacrificed in favor of scalability, and vice versa. In an effort to preserve both properties, we present here an extended finite element method (XFEM) for large scale composite fracture simulations. In this formulation, the standard FEM formulation is partially enriched by use of shifted Heaviside functions with special attention paid to the scalability of the scheme. This enrichment technique offers several benefits since the interpolation property of the standard shape function still holds at the nodes. Those benefits include (i) no extra boundary condition for the enrichment degree of freedom, and (ii) no need for transition/blending regions; both of which contribute to maintaining the scalability of the code. Two different cohesive zone models (CZM) are then adopted to capture the physics of the crack propagation mechanisms. At the intralaminar level, an extrinsic CZM embedded in the XFEM formulation is used. At the interlaminar level, an intrinsic CZM is adopted for predicting the failure. The overall framework is implemented in ALYA, a mechanics code specifically developed for large scale, massively parallel simulations of coupled multi-physics problems. The implementation of both intrinsic and extrinsic CZM models within the code is such that it conserves the extremely efficient scalability of ALYA while providing accurate physical simulations of computationally expensive phenomena. The strong scalability provided by the proposed implementation is demonstrated. The model is ultimately validated against a full experimental campaig, A.J., A.M., D.T., L.N. and L.W. acknowledge funding through the SIMUCOMP ERA-NET MATERA + project financed by the Fonds National de la Recherche (FNR) of Luxembourg, the Consejería de Educación y Empleo of the Comunidad de Madrid, the Walloon region (agreement no 1017232, CT-EUC 2010–10-12), and by the European Unions Seventh Framework Programme (FP7/2007–2013)., Peer Reviewed, Postprint (author's final draft)

CFD modeling of turbulent free surface flows has become an important tool in the design of ship hulls. A two-fluid flow solver that can predict the flow pattern, free surface shape and the forces on the ship hull is presented. The discretization is based on unstructured linear finite elements, tetrahedras and prisms. A Variational Multiscale Stabilization technique known as Algebraic Sub Grid Scale Stabilization (ASGS) is used to deal with convection dominated flows and allow for equal order velocity–pressure interpolations. A fixed grid method that captures the position of the interface with the Level Set technique is used to simulate the two-phase flow. The jump in the fluid properties is smoothed in a region close to the interface. Spalart Allmaras and SST k–ω turbulence models have been tested without showing significant differences. The need to accurately predict the viscous forces on the ship hull has motivated the use of anisotropically refined prism elements close the hull. Such meshes have challenged the capabilities of the equation based reinitialization techniques that we had previously used for the Level Set method., Peer Reviewed, Postprint (author's final draft)

Single molecule manipulation techniques combined with molecular dynamics simulations and protein engineering have enabled, during the last decade, the mechanical properties of proteins to be studied directly, thereby giving birth to the field of protein nanomechanics. Recent data obtained from such techniques have helped gain insight into the structural bases of protein resistance against forced unfolding, as well as revealing structural motifs involved in mechanical stability. Also, important technical developments have provided new perspectives into protein folding. Eventually, new and exciting data have shown that mechanical properties are key factors in cell signaling and pathologies, and have been used to rationally tune these properties in a variety of proteins. Crown Copyright © 2009.