dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work started when the second author was visiting the  Basque Center for Applied Mathematics at Bilbao  in 2011. The second author thanks that institution for its hospitality and its support. The second author has also been supported by  Agence Nationale de la Recherche , Project  CISIFS , grant  ANR-09-BLAN-0213-02. The first and third authors have been partially supported by Grant  MTM2011-29306-C02-00  of the  MICINN, Spain , the  ERC Advanced  Grant  FP7-246775   NUMERIWAVES, ESF Research Networking Programme OPTPDE. The first named author has also been supported by the Grant  PI2010-04  of the  Basque Government.
The conception of a new turbomachinery design requires numerous operating envelopes of different geometries. The design process is, consequently, time-consuming due to high experimental cost. We proposed to use a new methodology with a parametrized geometry operating envelope using CFD computations. Our methodology involved an elusive art of meta-model based optimization. It is applied to the design of a new concept of engine cooling fan, the aim of which is to find a design of weaker torque and higher static efficiency. Firstly, a new concept engine cooling fan has been proposed to better integrate the underhood environment of the automotive vehicle. From this design, an optimization is done with the method. By a mesh morphing technique based on the Radial Basis Function, the computational geometry is parametrized, so that, a geometry database is constructed.
Proposed methodology Regarding the classical fan blade design procedure, there are mainly 4 principal elements : • A procedure to describe and modify the mesh through its geometrical control parameters ; • A numerical simulation with classical solver ; • A database building with meta-model ; • An optimization method. The 4 steps are fulfilled by 4 tools in the current study : • Turb'Mesh : continuous unstructured mesh deformation that depends on geometrical parameters; • SC/Tetra : incompressible pressure based RANS finite volume solver with Menter turbulence model ; • Meta-model : database building with polynomial extrapolation ; • NSGA-2 : a genetic algorithm to explore database and to find the multi-objective solutions. A meta-model is constructed based on the new geometry with selected variables and objectives, and optimizations are realised to find several optima corresponding to different criteria.
Database building and optimization approach The meta-models are initially developed as surrogates of the expensive simulation process in order to improve the overall computation efficiency. The objectives can be directly extracted from the meta-model for specific parameter inputs. The parametrization technique provides significant advantages for understanding the flow behavior. The next step is to take advantage of this technique and construct a surrogate model which could actually be used for the optimization purpose. The parametrization study returns the derivatives of the objectives with respect to each parameter, which allows us to figure out the second order Taylor polynomials for 3 objective functions. The original configuration has an efficiency of 58. 16%, as summerized in Table  1.
All wells have an internal diameter of 0. 3048 m (1. 0 ft). The water properties are: -formation volume factor B w ¼ 1:01; -compressibility c w ¼ 4:5 Â 10 À5 bar À1 ; -viscosity l w ¼ 0:3 cp. On the other hand, we consider a dead oil with the PVT properties reported in Table  1. The relative permeabilities are derived from the Brooks-Corey models  [34] : k rw ¼ S À S wi 1 À S wi À S or 2 ð29Þ k r0 ¼ 1 À S À S wi 1 À S wi À S or 2 ð30Þ where S wi ¼ S or ¼ 0:2. The upscaling and flow simulation results presented hereafter were obtained using the CobraFlow software and the PumaFlow simulator  [35, 36] , respectively. In this work, we did not study the numerical dispersion due to the mathematical model of the simulator. Upscaling and coarsening cause numerical dispersion. In this work, the simulator adapts the time step itself to obtain correct results.
ACKNOWLEDGMENTS The authors would like to thank all the team involved in this development;  J. Kittel  (PhD),  S. Gonzalez  (Ing. ), P. L. Carrette  (PhD),  P. A. Bouillon  (Ing. ),  A. Gomez  (PhD),  B. Delfort  (PhD),  P. Alix  (Ing. ), R. Dugas  (PhD),  L. Raynal  (PhD),  K. Lettat  (PhD) and all the others. The authors would like to thank also ENEL for the really good collaboration that is done with IFP Energies nouvelles and will demonstrated the HiCapt + TM process performance at industrial pilot scale.
Résumé -Simulations atomistiques automatiques et systématiques dans l'environnement logiciel de MedeA Ò : Application à EU-REACH -Ce travail démontre notre capacité à prédire systématiquement les propriétés thermodynamiques par lot de plusieurs milliers de molécules en utilisant des procédures automatiques. Ceci est accompli à l'aide de nouveaux outils et fonctions intégrés dans l'environnement logiciel de MedeA Ò (Material Exploration and Design Analysis), qui manipule l'exécution automatique de séquences de tâches sur de grands nombres de molécules comprenant la création de modèles moléculaires 3D à partir de représentations 1D, l'exploration systématique des conformations possibles pour chaque molécules, la création et la soumission de tâches informatiques pour calculer des propriétés sur des ordinateurs parallèles, et le post-traitement par comparaison avec les propriétés experimentales disponibles.
INTRODUCTION Within the context of the European Union Registration, Evaluation, Authorization and restriction of CHemicals protocol (EU-REACH)  [1] , a daunting number of chemical compounds has to be characterized in the form of at least 17 physico-chemical properties for each compound. Furthermore, the properties need to be determined not only for pure compounds, but also for any formulations of the compounds under which they are commercialized and transported. Around 17 000 compounds have been pre-registered already in the REACH database  [2] , and all compounds are required to be fully registered by the end of 2018. This is a large number of systems, yet it represents only a fraction of the 'known' existing chemical compounds: for instance, more than 88 million chemical substances were registered in the Chemical Abstract Service (CAS) database of the American Chemical Society as of May 2014  [3].
In fact, most of the software developments and results described here originate from this project. Several objectives were defined in the PREDIMOL project. First of all, we wanted to establish the minimum input information needed to compute the desired physico-chemical properties. Ideally only the chemical structure and formulation in case of mixture should be necessary. The second important set of tasks, which was distributed among the different partners of the PREDIMOL project, was to determine the error associated with the evaluation of the properties with respect to experimental reference data using different computational methods. This information also provides valuable guidelines for the range of applicability of various computational methods. To be as complete as possible, this second task included both a comprehensive literature review and an applicative program to extend the scope of the evaluations done in the literature.
As stated earlier, one of our contributions to the PREDIMOL research project was to evaluate the accuracy of particular simulation methods. This was not our main and only contribution. The most important tasks were the design and development of software capabilities to prepare, submit, and analyze very large number of atomistic simulations, typically on the order of 1 000, possibly in a single batch, and to verify with real case applications the validity and robustness of the tools. When one handles such a large number of atomistic simulations, it becomes nearly impossible to manually prepare the different jobs. Hence, an effort of automation of the 'routine' atomistic simulation preparation procedure was done. The next important contribution was to identify default simulation parameters that result in stable and successful simulations.
After one submits a large batch of calculations, it is not acceptable to manually intervene to correct problems that occurred for hundreds of them because of ill-defined input simulation parameters. This task of optimization of the simulation protocols and parameters is therefore very important and should be completely transparent to the end-user once successfully implemented. In the following, we will first provide an overview of the MedeA Ò software environment before we explain the software functionalities and features that were added to achieve the objective of high-throughput atomistic simulations as specified in global projects like EU-REACH. To illustrate the capacities of the software and its environment, we describe selected applications of direct relevance for EU-REACH.
Selection of Molecules and Property Data For organic molecules Simplified Molecular Input Line Entry Specification (SMILES) formulas were extracted from the DIPPR database  [12]. This was done for 880 molecules representing 15 classes of organic molecules (Tab. 1). The search criteria in the DIPPR database included all molecules of the different classes mentioned in Table  1  containing from 1 to 9 carbon atoms. The liquid densities at 298 K and 1 bar of these molecules and their standard heats of formation were also collected from the DIPPR. In the DIPPR database, the data and properties are not always experimental. They can also be predicted, extrapolated, and interpolated. The liquid densities are obtained from empirical relationships with an accuracy of 1% with respect to experimental data within the range of temperature applicability defined for each molecule.
The temperature range of the molecular liquid density is typically bracketed by the melting and boiling temperatures at 1 bar. Out of the 880 molecular systems we computed the density for a subset of 192 systems. We checked that all empirical molecular liquid densities are in the temperature range of applicability for a temperature of 298 K. For the heat of formation of the molecules, we restricted the set to those systems where the experimental data have an accuracy of better than 5%. The experimental frequencies of vibration of the molecules were collected from the National Institute of Science and Technology (NIST) database  [13]. The ideal gas heat capacities at constant pressure of the organic molecules were obtained from tabulated empirical values in Poling et al. [14]. These empirical values are reported with a precision of 1% with respect to experimental values.
Computational Methods The equilibrium structure of all selected molecules was computed using Molecular Orbital PACkage (MOPAC)  [17]  with the PM7 semi-empirical method  [18]  as implemented in MedeA Ò. The geometry optimizations were confirmed by vibrational frequency calculations in the rigid molecule harmonic approximation: molecules are considered to be in their ground state when there is no imaginary frequency of vibration. The absence of imaginary frequencies signifies a local minimum. The corresponding molecular structures are assumed to represent the ground state. It is understood that this is an approximation. Density Functional Theory (DFT) geometry optimization were also performed using Turbomole  [19]  with the BP86  [20, 21]  and B3LYP  [20, 22, 23]  functionals with the Triple Zeta Valence plus Polarization (TZVP) basis set  [24]  followed by frequency calculations on all molecules.
Again, the quality of the geometry optimization was monitored through the absence of imaginary modes of vibration. The liquid density calculations were done using the molecular mechanics and molecular dynamics code Largescale Atomistic/Molecular Massively Parallel Simulator (LAMMPS)  [25] , as integrated in MedeA Ò. For all simulations, the Polymer Consistent ForceField-enhanced version (PCFF+) was used  [26]. This forcefield is derived from PCFF  [27]  and is enhanced to reproduce with high precision the properties and geometries of organic molecules in addition to those of the polymers. To compute the densities, cells containing 256 molecules were built. They were equilibrated in the isothermal-isobaric (NPT) ensemble for 1 ns with an integration time step of 0. 5 fs. The sampling time to collect the density values was 2 ns.
The MedeA Ò Software Environment MedeA Ò is characterized by a three-tier architecture (Fig. 1 ). The first level in the architecture is a Graphical User Interface (GUI), which enables the query and retrieval of atomistic structural information from integrated crystallographic databases  [30] [31] [32] , the import from external files in various formats (e. g. , Crystalloagraphic Information File (CIF) and Protein Data Bank (PDB)), or the direct construction of periodic or molecular atomistic models, the definition of atomistic simulation protocols (flowcharts) via graphical modular construction, and finally the graphical analysis of the simulation results. It is supported for different computer Operating Systems (OS), namely, Windows and Linux OS. After a simulation flowchart is created via the GUI, it is transmitted to the second level of MedeA Ò , namely the MedeA-JobServer.
The user interface of the JobServer(s) is accessed via any web browser interface (including tablets and cell phones) through its HyperText Transfer Protocol (http) address, optionally also in secure (https) mode. The third level in the MedeA Ò architecture consists of the TaskServers. This level encapsulates all numerically intensive simulations and interacts directly with the compute-servers. The TaskServer and the associated executables of the simulation programs are based on a light-weight software architecture so that they can be readily ported to high-performance computing platforms. The TaskServers receive their instructions from the JobServer and transmit the results back to the JobServer, which stores the relevant information. The users have access to all these data via the JobServer interface or via direct login in the computers themselves.
Direct access to the simulation files is generally unnecessary because the data post-treatment and summary of the main simulation results can be accessed via MedeA Ò 's GUI and the JobsServer's web interface. A range of atomistic simulation packages are integrated in the MedeA Ò 's simulation environment, namely, Vienna Ab initio Simulation Package (VASP)  [33] [34] [35] , LAMMPS  [25] , Gibbs  [36] , and MOPAC  [17].
Automation of the Preparation, Processing, and Analysis of Simulations The main additions to the MedeA Ò environment to permit preparing and submitting large number of atomistic simulations are first the possibility of defining and regrouping list of atomistic structures. A large versatility and flexibility is given to the user as to which structures can be added to this list. The atomistic structures can be imported from different atomistic file formats, from SMILES formulas [37], from the MedeA Ò canvas (and hence from the crystallographic databases that can be accessed from MedeA Ò ). Additionally, the list of structures can also be generated directly from a simulation itself and used as soon as created inside the sim- The three tier architecture of the MedeA Ò atomistic simulation software environment.
ulation protocol. All structure lists can be exported to other file formats if so desired by a user. After the list of structures is created, it can be parsed in a flowchart when the user adds a module that will iterate over each structure. The operations that are performed on the structures are defined in a sub-flowchart that is equivalent to a flowchart that is defined for a single structure. The module looping over structure list is fully integrated in the MedeA Ò 's flowchart paradigm and can be used in connection with other task modules, in particular those that manipulate the structures via translation of atoms, supercell building, amorphous phase building, random atomistic substitution and atomistic simulation. From a given list of structures, this allows constructing a much larger number of structures that include defects, etc.
g. , if the system is gaseous, liquid, or solid, if the systems is composed of small, medium, large molecules, or polymers (cross-linked or not). The definition of the tasks as done in the MedeA's software environment via graphical flowchart is particularly well-suited here: because the modular construction of the simulation permits easily to standardize protocols; because the flowcharts are electronic files that can be saved locally or collected from the GUI from the JobServer after the jobs ran by any of the users accessing the JobServer. This infrastructure constitutes an efficient way to conserve the knowledge and expertise of past campaigns of simulations (Fig. 1 ). A full flowchart for computing the liquid density of organic compound is shown in Figure  8. This figure is obtained from actual snapshots from MedeA's GUI.
The trade-off for improved accuracy is an increase in computational time. To lower the wall-clock time to results for large datasets, one can use Highly-Parallel Computers (HPC) that can currently perform up to 10 15 FLoating point Operations Per Second (petaFLOPS) while the exaFLOPS limit is estimated to be crossed in about 10 years from now. In the present work, we have successfully computed properties of up to 880 molecules. This raises the question if the current software is able to handle efficiently and with good stability an order of magnitude more molecules? This is a critical question since there is a dire shortage of experimental physico-chemical data. For instance, Fink and Reymond  [46]  demonstrated that a set of 26. 4 million chemically possible molecules (110. 9 million stereoisomers) can be generated from a pool of up to 11 atoms of C, N, O, and/or F.
The possibility of formation of the molecules included simple valency, chemical stability, and synthetic feasibility criteria. They analyzed that only 63 857 compounds of up to 11 atoms can be found in public databases. Systematic simulations on large datasets, e. g. on molecules with all possible isomeric forms and ranges of combinatorial substitutions, constitutes a most valuable source of comprehensive and consistent molecular and materials property data. In this context, we performed atomistic simulations on 5 869 molecules. As described above, the test started from SMILES formulas that were collected from accessible lists  [47, 48]. We restricted this list to molecules that possess a CAS name and number. The composition of the molecules in this list is indicated in Figure  10. All molecules are carbon-atom containing molecules, such as alkanes, olefins, aromatics, halogenated hydrocarbons, oxygenated hydrocarbons, thiols, sulfides, as well as hydrocarbons with less common elements, namely, B, Al, Si, P, Ge, As, Se, Sn, Sb, Tl, and Pb.
In other words, a more conservative choice of computational parameters in this quantum chemical method led to a remarkable success rate. The full list of molecules was processed in about 24 hours when the tasks are submitted simultaneously by batch of two using one CPU each. It was unnecessary to use more than one CPU to optimize the geometries and compute the frequencies of vibration with MOPAC of a molecule. This would be, however, different for other atomistic simulations. In the determination of the density using LAMMPS, the simulations were submitted by batches of 6 molecules at once each using 8 processors: other combinations are possible and depend on the available computer resources, and the intrinsic efficiency of the atomistic simulation software with respect to the size and nature of the systems to be simulated and hardware resources themselves  [45].
CONCLUSIONS We have presented the atomistic and molecular simulation software environment MedeA Ò and the new enhancements that have been incorporated to address the case of large number of simulations as will be needed for the systematic determination of molecular and materials properties, for instance in fulfillment of the requirements of EU-REACH. The new capabilities of MedeA Ò make the determination of properties for lists of several thousand molecules readily doable using atomistic simulation methods. The approach can be extended to larger datasets due to its inherent parallel characteristics and robustness, thus setting the stage for fully exploiting the growth in computer power. We have shown that the preparation of the simulations can be made very straightforward and simple. The tasks of creating molecular models and setting up the simulation parameters are largely automated.
5% for organic and 4. 8% for inorganic compounds at 298 K. These values are obtained with fast (compared to DFT) semi-empirical method. We also analyzed with great detail the evolution of the thermochemical values as a function of temperature, in particular the organic molecule ideal gas heat capacities. The volumetric masses of 192 organic fluids were also investigated and validated with an accuracy of 2. 4% with respect to experimental data. In summary, the MedeA Ò software environment with the recent enhancements set the stage  for the large-scale deployment of quantum mechanical and forcefield-based simulations as an extremely efficient source of molecular and materials properties.
ACKNOWLEDGMENTS This work was carried out within the project  PREDIMOL  funded by the  French Research Agency  under contract  ANR-2010-CD2I-007-05  in partnership with ARKEMA,  IFP Energies nouvelles, INERIS, ENSCP ParisTech, University Paris Sud 11 , and  CNRS  to provide software solutions to industry within the context of the  EU-REACH  protocol.
Sur la gauche, le narrateur est Poppy (un des quatre personnages animés de la plate-forme SEMAINE  (Schröder, 2010) ), alors qu'il s'agit sur la droite d'un flux vidéo. Poppy a été utilisée afin d'avoir un narrateur féminin en phase de magicien d'Oz tout comme en phase de visio-conférence. Cette configuration nécessite deux webcams, ce qui nous a permis de filmer l'enfant à partir d'angles différent. Le flux vidéo récupéré est aussi envoyé à la vue de pilote. L'installation de la vidéo conférence est effectuée en utilisant plusieurs canaux de communication, construite avec Gstreamer pour Linux  (Taymans et al. , 2001). Toutes les vidéos récupérées sont enregistrées en copies multiples, pour assurer la sauvegarde. Les illustrations de l'histoire se trouvent à gauche des narrateurs. Les images sont numérisées et synchronisées entre les 3 vues.
SCHWARZ METHODS We start with the original Schwarz algorithm  [3]  at the continuous (i. e. partial differential equations) level whose parallel version is named Jacobi-Schwarz Method (JSM). We introduce two variants that are at the origin of the popular Additive Schwarz Method (ASM) and Restricted Additive Schwarz (RAS  [4] ) algorithms. The first one has been the subject of hundreds of papers (see  [1]  and references therein). The second one is the default parallel solver of the parallel package software PETSc  [5]. This presentation shows in a unified setting the connections between these three algorithms.
First Numerical Tests We solve the model problem (15) on the domain X = [0, 1] 2 using standard continuous, piecewise linear (P 1 ) finite elements. The diffusion a is a function of x. The boundary condition is u = 0 on the left side boundary and @u @n ¼ 0 on the remainder. The corresponding discretizations and data structures were obtained by using the software FreeFem++  [18]  in connection with the METIS partitioner  [19]. We will test the Standard Additive Ω i -1 Ω i Ω i +1 Figure 7 1D example with many high heterogeneities islands. Schwarz (SAS) and the RAS preconditioners with and without coarse space, in particular comparing the new coarse space based on harmonic extensions of eigenvectors of the local DtN operators with the standard coarse space that is the piecewise constant space of Nicolaides  [11].