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dc.contributor.authorNarayanan, Kiran
dc.contributor.authorMora Cordova, Angel
dc.contributor.authorAllsopp, Nicholas
dc.contributor.authorEl Sayed, Tamer S.
dc.date.accessioned2015-08-03T09:57:43Z
dc.date.available2015-08-03T09:57:43Z
dc.date.issued2012-07-17
dc.identifier.citationNarayanan, K., Mora, A., Allsopp, N., & Sayed, T. E. (2012). A hybrid, massively parallel implementation of a genetic algorithm for optimization of the impact performance of a metal/polymer composite plate. The International Journal of High Performance Computing Applications, 27(2), 217–227. doi:10.1177/1094342012451474
dc.identifier.issn10943420
dc.identifier.doi10.1177/1094342012451474
dc.identifier.urihttp://hdl.handle.net/10754/562242
dc.description.abstractA hybrid parallelization method composed of a coarse-grained genetic algorithm (GA) and fine-grained objective function evaluations is implemented on a heterogeneous computational resource consisting of 16 IBM Blue Gene/P racks, a single x86 cluster node and a high-performance file system. The GA iterator is coupled with a finite-element (FE) analysis code developed in house to facilitate computational steering in order to calculate the optimal impact velocities of a projectile colliding with a polyurea/structural steel composite plate. The FE code is capable of capturing adiabatic shear bands and strain localization, which are typically observed in high-velocity impact applications, and it includes several constitutive models of plasticity, viscoelasticity and viscoplasticity for metals and soft materials, which allow simulation of ductile fracture by void growth. A strong scaling study of the FE code was conducted to determine the optimum number of processes run in parallel. The relative efficiency of the hybrid, multi-level parallelization method is studied in order to determine the parameters for the parallelization. Optimal impact velocities of the projectile calculated using the proposed approach, are reported. © The Author(s) 2012.
dc.description.sponsorshipThis work was fully funded by the KAUST baseline fund.
dc.publisherSAGE Publications
dc.subjectcomposite
dc.subjectcomputational steering
dc.subjectductile fracture
dc.subjectimpact
dc.subjectoptimization
dc.subjectparallel genetic algorithm
dc.titleA hybrid, massively parallel implementation of a genetic algorithm for optimization of the impact performance of a metal/polymer composite plate
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentCore Labs
dc.contributor.departmentInvestment Fund
dc.contributor.departmentKAUST Supercomputing Laboratory (KSL)
dc.contributor.departmentMechanical Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalInternational Journal of High Performance Computing Applications
dc.contributor.institutionCray Computing, Stuttgart, Germany
kaust.personAllsopp, Nicholas
kaust.personNarayanan, Kiran
kaust.personMora Cordova, Angel
kaust.personEl Sayed, Tamer S.
dc.date.published-online2012-07-17
dc.date.published-print2013-05


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