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A Sparse Grid Collocation based Parametric Exploration Method for Electronics Cooling
Keywords: Simulation, Parametric Exploration, Sparse Grid Collocation method
Computer simulations have become an indispensable tool in the design process of electronics systems and sub-systems. The improvements in processor speeds and availability of large and relatively inexpensive memory has brought made large-scale problems possible at relatively low costs. The simulations are capturing ever more details of geometry and physics of the problems to make them acceptably accurate and so have become a part of the predictive design process. As the details included in the models have increased, the processing time (turn-around time for simulations) have only increased. In addition, the predictive design requires exploration of design space, be it geometry or operating condition or material selection, for the most cost effective solution that is manufacturable. This typically requires computations of sensitivities and parametric space exploration. The parametric simulations have also become a common practice in design process. Most modern electronics design are governed or impacted by multiple parameters including many geometric parameters, material properties, power levels, environmental conditions, etc. A comprehensive exploration of the parametric space still remains cost prohibitive even with the modern compute advances. DoE techniques are employed to cull the parametric space in some cases or often an ad hoc selection of simulations are performed at selected parametric values and the design impacts derived from them. In all these cases, the full space exploration is compromised. In this work, we present a sparse grid collocation method based approach that allows for characterizing the design space for multi-parameter cases. The method is shown to be efficient with over 100 to 1000 times speed up in characterizing the space for package design and forced-flow system design governed by ten or more parameter. The multi-dimensional (over 10 dimensions in the parametric space) entire space can be characterize to second-order accuracy with 21 simulations. The paper will present the details of the methodology and discuss the results for the two sample cases with response surfaces for combination of parameters. The paper will also discuss the modes of visualizing and extracting useful information on quantities of interest from this multidimensional data.
Prabhu Sathyamurthy, Director, CFD Business Unit
ESI Group
Austin, TX

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