Multi-condition optimization of a cross-flow fan based on the maximum entropy method

Abstract

Cross-flow fans are widely used in heating, wind-curtains, and air-conditionings, as well as other ventilation systems. A single or double arc is generally used as the camber line of cross-flow fans, but this design leads to constraints in the geometry of the blade profiles. In this study, the camber line of a cross-flow fan blade was parameterized by five parameters based on the fourth-order Bezier curve. A two-dimensional computational fluid dynamics (CFD) simulation was conducted to predict the aerodynamic characteristics and the internal flow field. It is necessary in multi-condition optimization, to evaluate the relative importance of the performance parameters under different working conditions and determine their weight factors. Here, a novel maximum entropy method (MEM) was proposed to quantify of volume flow rate, because the method avoids the subjectivity in the selection of the weights. Subsequently, a multi-island genetic algorithm (MIGA), combined with numerical simulation, was used to search the global optimum in the given design space. The results indicated that the optimum combination of the structural parameters reduced the blade channel vortex in a particular location of the impeller and changed the position and size of the eccentric vortex. The volume flow rate of the optimized impeller was 4.28% higher at the minimum rotation speeds and 12.87% higher at the maximum rotation speeds.