MPI-enabled Shape Optimization of Panels Subjected to Air Blast Loading

Abstract

-The problem of finding the optimal shape of an aluminum plate to mitigate air blast loading is considered. The goal is to minimize the dynamic displacement of the plate relative to the test fixture, while monitoring plastic strain values, mass, and envelope constraints. This is a computationally challenging problem owing to (a) difficulty with finding optimal shapes with higher dimensional shape variations, (b) non-differentiable, non-convex and computationally expensive objective and constraint functions, and (c) difficulties in controlling mesh distortion that occur during explicit finite element analysis. An approach based on coupling LS-DYNA finite element software and a differential evolution (DE) optimizer is presented. Since DE involves a population of designs which are then crossed-over and mutated to yield an improved generation, it is possible to use coarse parallelization wherein a computing cluster is used to evaluate fitness of the entire population simultaneously. However, owing to highly dissimilar computing time per analysis, a result of mesh distortion and minimum time step in explicit finite element analysis, implementation of the parallelization scheme is challenging. Sinusoidal basis shapes are used to obtain an optimized 'double-bulge' shape. = maximum plastic strain at failure ε = equivalent plastic strain M = total mass of the structure M max = upper limit for the mass of the structure T = thickness of the structure (plate) at any (x, y) location in the plate t min = Minimum thickness allowed z = vector of z-coordinate of the nodes z U = upper limit on z-coordinate z L

Efficient supersonic air vehicle design using the Service-Oriented Computing Environment (SORCER)

“…Using this technology, SORCER implements a self-load balancing grid computing system that can dynamically grow and shrink during the course of an optimization study. The self-load balancing quality of SORCER is necessary to ensure efficient use of computational resources given the varying computational expense of different tasks executing simultaneously during a design study [13].…”