In this paper, concepts are investigated for tuning material properties and component configurations in order to design structures with unique dynamic characteristics for mitigating blast loads while maintaining or reducing weight. The Dynamic Response Index (DRI) is employed as an occupant injury metric for determining the effectiveness of the each blast mitigation configuration that is considered. A finite element model of a notional V-Hull structure is used as a numerical example in this study. The material properties and the configuration of the inner bulkheads that connect the V-shaped outer surface with the inner floor are used as design parameters for reducing the DRI at a typical occupant location. In this particular example, it is demonstrated that both the weight of the structure and the DRI can be reduced simultaneously. This is achieved by creating a new structural design that features energy absorbing and decoupling mechanisms among the bulkheads, floor, seat, and the occupant.
Most important journal papers in magnetics are selected from conference records with quick review and subject to stringent page limits. The literature as a result is unsatisfactory, inadequately attributing previous works and without sufficient details to replicate work presented. This paper therefore reviews mathematical optimization in synthesis and nondestructive evaluation (NDE) by the finite element method in magnetics. The review identifies the earliest papers. Thereafter this paper proposes and establishes the feasibility of coupled problem optimization using the genetic algorithm to avoid mesh induced minima which hurt gradient based methods. The genetic algorithm, while avoiding the need for derivatives, results in having to undertake even more numerous finite element solutions. Although the genetic algorithm has been applied in optimization, in coupled systems the number of object function evaluations doubles. We there examine the use of graphics processing units (GPUs) to handle the immense computational load. GPUs have recently been introduced in finite element analysis but their memory limits are often not recognized and are critically limiting when parallelizing the several solutions required in optimization. To overcome this limit, element-by-element finite element matrix processing is employed, making coupled problems practicable on GPUs. We overcome the memory limits faced by others.
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