We present a detailed theoretical model of the rotational molding process, and identifj, the key dimensionless groups affecting the process cycle time. This theoretical model is employed to create differential and lumped parameter numerical models, as well as a simple closed form estimate for the time required for complete powder deposition. Both numerical models give results that are in very good agreement with experimental data available in the literature. The closed form solution gives good predictions over a wide range of processing parameters. In addition, the effects of variations in the dimensionless groups on processing time are evaluated.

We present a detailed theoretical heat transfer model for the entire rotational molding process (including heating and cooling stages) and identify the key dimensionless groups affecting the process cycle time. This theoretical model is employed to generate numerical results that are in very good agreement with the experimental data available in the literature. The effects of variations in the dimensionless groups on the cycle time are evaluated. In addition, part shrinkage has been incorporated in the models, and its effect on the process cycle time has been studied extensively.

In recent years, increasing effort has been made by the cluster and grid computing community to build object-based Distributed Shared Memory systems (DSM) in a cluster environment. In most ofthese systems, a shared object is simply used as a data-exchanging unit so as to alleviate the false-sharing problem, and the advantages ofsharing objects remain to be fully exploited. Thus, this paper is motivated to investigate the potential advantages ofobject-based DSM. For example, the performance ofa distributed application may be significantly improved by adaptively andjudiciously setting the size of the sharedobjects, i.e., granularity. This paper, in addition to investigating the advantages of sharing objects, particularly focuses on observing how the performance of a distributed application changes with varied granularity, obtaining the optimal granularity through curvefitting, studying the factors that affect the optimal granularity, and predicting this optimal granularity in a changing runtime environment.

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