Driving energy efficiency with design optimization of a centrifugal fan housing system for variable frequency drives

Increasing demands for electrical equipment efficiency has renewed interest in improving the electrical and thermal efficiency of industrial equipment. Variable speed drives are typically air-cooled and fall under recent international standards for improved fan efficiency. This paper details the modeling, design optimization, and experimental verification approaches used to optimize blower housing designs for variable speed drives. The design of the blower housing is just as important as the blower selection. By modifying the housing dimensions, the shape and quantity of flow exiting the housing can be controlled. First, the impeller and inlet ring geometry was directly imported into Icepak from a CAD model provided by the manufacturer. Moving Reference Frame and multi-level meshing techniques were used to provide an accurate representation of the air flow. Next, a flow-pressure curve was created by varying the outlet pressure. The blower performance curve was found to follow, but consistently under-predict the empirical fan curve data given by the manufacturer. Using fan laws and a multi-objective optimization approach, a model fan speed that was 2.4% higher than the operating speed was found to make the predicted and manufacturer performance data agree with less than a 3% error. Next, a parametric model of the blower housing was created in Icepak using the tuned blower model. Four parametric variables included the distance from the impeller to the front, bottom, side, and back of the housing was chosen. A fifth variable, pressure was chosen so that the effect of outlet pressure on flow could be extracted. The blower housing was optimized using a Design of Experiments (DoE) technique where the geometry of housing was varied in a structured manner to capture expected second order behavior. The 27-run DoE was performed in Icepak and the volumetric flow through discretized portions of the outlet were recorded. The DoE data for each section of the outlet were fit to equations using a backward regression technique. A genetic algorithm-based optimization technique was used to create housing designs for two different variable frequency drives. Prototypes of the housings were constructed for each design and flow-pressure curves for three samples of each design were measured on a flow bench. The measured curves were found to agree with the predicted blower performance in each housing design to within 7%. Design curves that could be used for other housings were also generated.