Grid -based Computing for Multidisciplinary Analysis and Optimization

Multidisciplinary design demands the simulation of complex systems in order to evaluate the behaviour of these systems under different objectives and control strategies. Finding a “good” – or even maybe an optimal – design for a system implies the solution of an optimization problem based on the results of typically hundreds to thousands simulations. The application of computationally expensive simulations in the course of such multidisciplinary optimizations results in long -running solution processes even when using state -of -the -art parallel/distributed algorithms and hardware. The grid -based solution of this kind of optimization problems demands certain features of parallel/distributed systems: efficient utilization of resources (i.e. processors), adequate optimization algorithms (i.e. inherently parallel optimization algorithms), and software integration (i.e. integration of optimization algorithms and simulation code). In this paper, multidisciplinary optimization tasks are characterized and a grid -based problem solving environment with a corresponding scalable algorithm is presented. The usability of t he approach is demonstrated by applying it to different problems from groundwater engineering, automotive industry and airplane design.