A hybrid, massively parallel implementation of a genetic algorithm for optimization of the impact performance of a metal/polymer composite plate. (May 2013)

• Record Type: Journal Article
• Authors: Narayanan, Kiran; Mora, Angel; Allsopp, Nicholas; Sayed, Tamer El
• Is Part Of: International journal of high performance computing applications. Volume 27:Number 2(2013)
• Page Start: 217
• Page End: 227
• Publication Date: 2013-05
• Subjects: parallel genetic algorithm -- computational steering -- optimization -- impact -- ductile fracture -- composite; High performance computing -- Periodicals; Supercomputers -- Periodicals; 004.1105
• DOI: 10.1177/1094342012451474
• Languages: English
• ISSNs: 1094-3420

Abstract

A 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.