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A CFD Optimization of Airflow Efficiency for an Electric Motor Driven Centrifugal Fan System for Residential HVAC Applications

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Interdisciplinary Topics in Applied Mathematics, Modeling and Computational Science

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 117))

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Abstract

This study focuses on the design and optimization of an electronics enclosure intended for use in a centrifugal fan being driven by an electric motor for the residential heating, ventilating, and air conditioning(HVAC) market. Typically in these systems the motor is mounted directly in the airstream of the centrifugal fan, but in this case the Regal Beloit’s axial motor technology allows for the minimization of this obstruction to the airflow. In the system analyzed the axial motor is mounted in the center of the centrifugal fan and the electronics used to drive the system is enclosed and mounted to the axial motor. This enclosure has been optimized for system airflow efficiency and thermal management of the electronics. A sensitivity analysis was also performed to understand the optimized design’s performance under various application environments. Computational fluid dynamics (CFD) was used as a test platform and tool for optimization. The CFD analysis was driven by goal optimization software to explore the design space and lead to an optimized design for overall efficiency. Results were validated to test data and test visualization methods. This presentation will cover the design requirements and details of the application, the optimization and CFD techniques used, and the criteria used for CFD model validation.

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Correspondence to Rachele Cocks .

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Cocks, R., Westhoff, J. (2015). A CFD Optimization of Airflow Efficiency for an Electric Motor Driven Centrifugal Fan System for Residential HVAC Applications. In: Cojocaru, M., Kotsireas, I., Makarov, R., Melnik, R., Shodiev, H. (eds) Interdisciplinary Topics in Applied Mathematics, Modeling and Computational Science. Springer Proceedings in Mathematics & Statistics, vol 117. Springer, Cham. https://doi.org/10.1007/978-3-319-12307-3_21

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