Scalar mass conservation in turbulent mixture fraction based combustion models through consistent local flow parameters

Mixture fraction-based models are widely employed for predicting turbulent non-premixed combustion processes due to their cost-effectiveness and well-established subfilter closure. In these models, the transport of reactive scalars in physical space is decomposed into two components: scalar transport relative to mixture fraction and transport of mixture fraction in physical space. Conventional flamelet models do not consider that these two processes have to be formulated consistently, which can lead to scalar mass conservation errors. In the context of multiphase flows, scalar transport in mixture fraction space is governed by three conditional flow-dependent parameters: the conditional scalar dissipation rate, the conditional scalar diffusion rate, and the conditional spray source term. The evolution of mixture fraction in physical space is typically modeled using the presumed Filtered Density Function (FDF) approach. This paper introduces a novel formulation for the conditional flow parameters that aligns with the presumed FDF approach, thereby ensuring scalar mass conservation. The proposed model is applied to a Large-Eddy Simulation (LES) of the inert ECN Spray A case, with a comparison against a conventional flow parameter model that employs an inverse error function shape for the scalar dissipation rate. The results indicate that the conventional model produces similar conditional dissipation rates to the new model in regions where combustion takes place. However, significant discrepancies are observed in the conditional diffusion rate, highlighting the susceptibility of the conventional model to scalar mass conservation errors for non-unity Lewis number scalars

INDIGO-DataCloud: a Platform to Facilitate Seamless Access to E-Infrastructures

[EN] This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to manage the demanding requirements of scientific communities, either locally or through enhanced interfaces. The middleware developed allows to federate hybrid resources, to easily write, port and run scientific applications to the cloud. In particular, we have extended existing PaaS (Platform as a Service) solutions, allowing public and private e-infrastructures, including those provided by EGI, EUDAT, and Helix Nebula, to integrate their existing services and make them available through AAI services compliant with GEANT interfederation policies, thus guaranteeing transparency and trust in the provisioning of such services. Our middleware facilitates the execution of applications using containers on Cloud and Grid based infrastructures, as well as on HPC clusters. Our developments are freely downloadable as open source components, and are already being integrated into many scientific applications.INDIGO-Datacloud has been funded by the European Commision H2020 research and innovation program under grant agreement RIA 653549.Salomoni, D.; Campos, I.; Gaido, L.; Marco, J.; Solagna, P.; Gomes, J.; Matyska, L.... (2018). INDIGO-DataCloud: a Platform to Facilitate Seamless Access to E-Infrastructures. Journal of Grid Computing. 16(3):381-408. https://doi.org/10.1007/s10723-018-9453-3S381408163García, A.L., Castillo, E.F.-d., Puel, M.: Identity federation with VOMS in cloud infrastructures. In: 2013 IEEE 5Th International Conference on Cloud Computing Technology and Science, pp 42–48 (2013)Chadwick, D.W., Siu, K., Lee, C., Fouillat, Y., Germonville, D.: Adding federated identity management to OpenStack. Journal of Grid Computing 12(1), 3–27 (2014)Craig, A.L.: A design space review for general federation management using keystone. In: Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, pp 720–725. IEEE Computer Society (2014)Pustchi, N., Krishnan, R., Sandhu, R.: Authorization federation in iaas multi cloud. In: Proceedings of the 3rd International Workshop on Security in Cloud Computing, pp 63–71. ACM (2015)Lee, C.A., Desai, N., Brethorst, A.: A Keystone-Based Virtual Organization Management System. In: 2014 IEEE 6Th International Conference On Cloud Computing Technology and Science (Cloudcom), pp 727–730. IEEE (2014)Castillo, E.F.-d., Scardaci, D., García, A.L.: The EGI Federated Cloud e-Infrastructure. Procedia Computer Science 68, 196–205 (2015)AARC project: AARC Blueprint Architecture, see https://aarc-project.eu/architecture . Technical report (2016)Oesterle, F., Ostermann, S., Prodan, R., Mayr, G.J.: Experiences with distributed computing for meteorological applications: grid computing and cloud computing. Geosci. Model Dev. 8(7), 2067–2078 (2015)Plasencia, I.C., Castillo, E.F.-d., Heinemeyer, S., García, A.L., Pahlen, F., Borges, G.: Phenomenology tools on cloud infrastructures using OpenStack. The European Physical Journal C 73(4), 2375 (2013)Boettiger, C.: An introduction to docker for reproducible research. ACM SIGOPS Operating Systems Review 49(1), 71–79 (2015)Docker: http://www.docker.com (2013)Gomes, J., Campos, I., Bagnaschi, E., David, M., Alves, L., Martins, J., Pina, J., Alvaro, L.-G., Orviz, P.: Enabling rootless linux containers in multi-user environments: the udocker tool. Computing Physics Communications. https://doi.org/10.1016/j.cpc.2018.05.021 (2018)Zhang, Z., Chuan, W., Cheung, D.W.L.: A survey on cloud interoperability taxonomies, standards, and practice. SIGMETRICS perform. Eval. Rev. 40(4), 13–22 (2013)Lorido-Botran, T., Miguel-Alonso, J., Lozano, J.A.: A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments. Journal of Grid Computing 12(4), 559–592 (2014)Nyrén, R., Metsch, T., Edmonds, A., Papaspyrou, A.: Open Cloud Computing Interface–Core. Technical report, Open Grid Forum (2010)Metsch, T., Edmonds, A.: Open Cloud Computing Interface-Infrastructure. Technical report, Open Grid Forum (2010)Metsch, T., Edmonds, A.: Open Cloud Computing Interface-RESTful HTTP Rendering. Technical report, Open Grid Forum (2011)(Ca Technologies) Lipton, P., (Ibm) Moser, S., (Vnomic) Palma, D., (Ibm) Spatzier, T.: Topology and Orchestration Specification for Cloud Applications. Technical report, OASIS Standard (2013)Teckelmann, R., Reich, C., Sulistio, A.: Mapping of cloud standards to the taxonomy of interoperability in IaaS. In: Proceedings - 2011 3rd IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2011, pp 522–526 (2011)García, A.L., Castillo, E.F.-d., Fernández, P.O.: Standards for enabling heterogeneous IaaS cloud federations. Computer Standards & Interfaces 47, 19–23 (2016)Caballer, M., Zala, S., García, A.L., Montó, G., Fernández, P.O., Velten, M.: Orchestrating complex application architectures in heterogeneous clouds. Journal of Grid Computing 16 (1), 3–18 (2018)Hardt, M., Jejkal, T., Plasencia, I.C., Castillo, E.F.-d., Jackson, A., Weiland, M., Palak, B., Plociennik, M., Nielsson, D.: Transparent Access to Scientific and Commercial Clouds from the Kepler Workflow Engine. Computing and Informatics 31(1), 119 (2012)Fakhfakh, F., Kacem, H.H., Kacem, A.H.: Workflow Scheduling in Cloud Computing a Survey. In: IEEE 18Th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations (EDOCW), 2014, Vol. 71, pp. 372–378. Springer, New York (2014)Stockton, D.B., Santamaria, F.: Automating NEURON simulation deployment in cloud resources. Neuroinformatics 15(1), 51–70 (2017)Plóciennik, M., Fiore, S., Donvito, G., Owsiak, M., Fargetta, M., Barbera, R., Bruno, R., Giorgio, E., Williams, D.N., Aloisio, G.: Two-level Dynamic Workflow Orchestration in the INDIGO DataCloud for Large-scale, Climate Change Data Analytics Experiments. Procedia Computer Science 80, 722–733 (2016)Moreno-Vozmediano, R., Montero, R.S., Llorente, I.M.: Multicloud deployment of computing clusters for loosely coupled mtc applications. IEEE transactions on parallel and distributed systems 22(6), 924–930 (2011)Katsaros, G., Menzel, M., Lenk, A.: Cloud Service Orchestration with TOSCA, Chef and Openstack. In: Ic2e (2014)Garcia, A.L., Zangrando, L., Sgaravatto, M., Llorens, V., Vallero, S., Zaccolo, V., Bagnasco, S., Taneja, S., Dal Pra, S., Salomoni, D., Donvito, G.: Improved Cloud resource allocation: how INDIGO-DataCloud is overcoming the current limitations in Cloud schedulers. J. Phys. Conf. Ser. 898(9), 92010 (2017)Singh, S., Chana, I.: A survey on resource scheduling in cloud computing issues and challenges. Journal of Grid Computing, pp. 1–48 (2016)García, A.L., Castillo, E.F.-d., Fernández, P.O., Plasencia, I.C., de Lucas, J.M.: Resource provisioning in Science Clouds: Requirements and challenges. Software: Practice and Experience 48(3), 486–498 (2018)Chauhan, M.A., Babar, M.A., Benatallah, B.: Architecting cloud-enabled systems: a systematic survey of challenges and solutions. Software - Practice and Experience 47(4), 599–644 (2017)Somasundaram, T.S., Govindarajan, K.: CLOUDRB A Framework for scheduling and managing High-Performance Computing (HPC) applications in science cloud. Futur. Gener. Comput. Syst. 34, 47–65 (2014)Sotomayor, B., Keahey, K., Foster, I.: Overhead Matters: A Model for Virtual Resource Management. In: Proceedings of the 2nd International Workshop on Virtualization Technology in Distributed Computing SE - VTDC ’06, p 5. IEEE Computer Society, Washington (2006)SS, S.S., Shyam, G.K., Shyam, G.K.: Resource management for Infrastructure as a Service (IaaS) in cloud computing SS Manvi A survey. J. Netw. Comput. Appl. 41, 424–440 (2014)INDIGO-DataCloud consortium: Initial requirements from research communities - d2.1, see https://www.indigo-datacloud.eu/documents/initial-requirements-research-communities-d21 https://www.indigo-datacloud.eu/documents/initial-requirements-research-communities-d21 https://www.indigo-datacloud.eu/documents/initial-requirements-research-communities-d21 . Technical report (2015)Europen open science cloud: https://ec.europa.eu/research/openscience (2015)Proot: https://proot-me.github.io/ (2014)Runc: https://github.com/opencontainers/runc (2016)Fakechroot: https://github.com/dex4er/fakechroot (2015)Pérez, A., Moltó, G., Caballer, M., Calatrava, A.: Serverless computing for container-based architectures Future Generation Computer Systems (2018)de Vries, K.J.: Global fits of supersymmetric models after LHC run 1. Phd thesis Imperial College London (2015)Openstack: https://www.openstack.org/ (2015)See http://argus-documentation.readthedocs.io/en/stable/argus_introduction.html (2017)See https://en.wikipedia.org/wiki/xacml (2013)See http://www.simplecloud.info (2014)Opennebula: http://opennebula.org/ (2018)Redhat openshift: http://www.opencityplatform.eu (2011)The cloud foundry foundation: https://www.cloudfoundry.org/ (2015)Caballer, M., Blanquer, I., Moltó, G., de Alfonso, C.: Dynamic management of virtual infrastructures. Journal of Grid Computing 13(1), 53–70 (2015)See http://www.infoq.com/articles/scaling-docker-with-kubernetes http://www.infoq.com/articles/scaling-docker-with-kubernetes (2014)Prisma project: http://www.ponsmartcities-prisma.it/ (2010)Opencitiy platform: http://www.opencityplatform.eu (2014)Onedata: https://onedata.org/ (2018)Dynafed: http://lcgdm.web.cern.ch/dynafed-dynamic-federation-project http://lcgdm.web.cern.ch/dynafed-dynamic-federation-project (2011)Fts3: https://svnweb.cern.ch/trac/fts3 (2011)Fernández, P.O., García, A.L., Duma, D.C., Donvito, G., David, M., Gomes, J.: A set of common software quality assurance baseline criteria for research projects, see http://hdl.handle.net/10261/160086 . Technical reportHttermann, M.: Devops for developers Apress (2012)EOSC-Hub: ”Integrating and managing services for the European Open Science Cloud” Funded by H2020 research and innovation pr ogramme under grant agreement No. 777536. See http://eosc-hub.eu (2018)Apache License: author = https://www.apache.org/licenses/LICENSE-2.0 (2004)INDIGO Package Repo: http://repo.indigo-datacloud.eu/ (2017)INDIGO DockerHub: https://hub.docker.com/u/indigodatacloud/ https://hub.docker.com/u/indigodatacloud/ (2015)Indigo gitbook: https://indigo-dc.gitbooks.io/indigo-datacloud-releases https://indigo-dc.gitbooks.io/indigo-datacloud-releases (2017)Van Zundert, G.C., Bonvin, A.M.: Disvis: quantifying and visualizing the accessible interaction space of distance restrained biomolecular complexes. Bioinformatics 31(19), 3222–3224 (2015)Van Zundert, G.C., Bonvin, A.M.: Fast and sensitive rigid–body fitting into cryo–em density maps with powerfit. AIMS Biophys. 2(0273), 73–87 (2015

INDIGO-DataCloud: A data and computing platform to facilitate seamless access to e-infrastructures

This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to manage the demanding requirements of scientific communities, either locally or through enhanced interfaces. The middleware developed allows to federate hybrid resources, to easily write, port and run scientific applications to the cloud. In particular, we have extended existing PaaS (Platform as a Service) solutions, allowing public and private e-infrastructures, including those provided by EGI, EUDAT, and Helix Nebula, to integrate their existing services and make them available through AAI services compliant with GEANT interfederation policies, thus guaranteeing transparency and trust in the provisioning of such services. Our middleware facilitates the execution of applications using containers on Cloud and Grid based infrastructures, as well as on HPC clusters. Our developments are freely downloadable as open source components, and are already being integrated into many scientific applications

Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry

Background: Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients\u2019 clinical phenotypes and analyse the differential clinical course. Methods: We performed a hierarchical cluster analysis based on Ward\u2019s Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry. Results: A total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients\u2019 prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P <.001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27\u20133.62; HR 3.42, 95%CI 2.72\u20134.31; HR 2.79, 95%CI 2.32\u20133.35), and Cluster 1 (HR 1.88, 95%CI 1.48\u20132.38; HR 2.50, 95%CI 1.98\u20133.15; HR 2.09, 95%CI 1.74\u20132.51) reported a higher risk for the three outcomes respectively. Conclusions: In European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes