Aerodynamic optimisation of an industrial axial fan blade
Numerical optimisation methods have successfully been used for a variety of aerodynamic design problems over quite a few years. However the application of these methods to the aerodynamic blade shape optimisation of industrial axial fans has received much less attention in the literature probably given the fact that the majority of resources available to develop these automated design approaches is to be found in the aerospace field. This work presents the development of an automated design process which was developed to aerodynamically optimise the fan blade geometry. It involves the application of Genetic Algorithm (GA) methods to the aerodynamic shape optimisation of a two-dimensional axial fan cascade as well as the development of a three- dimensional shape optimisation routine. Navier-Stokes CFD codes were used for the 2 and 3-D analyses using steady simulations. The effects of the Variation of the control parameters on the performance of the GA as a optimisation tool is presented. The tournament selection, uniform crossover, creep mutation scheme with elitism appears to work the best of this application. The parallelisation of genetic algorithm was also developed using the Message Passing Interface (MPI) scheme. This essentially reduces the running time for each generation to the amount of time required for performing the genetic operations on just one individual. The aerodynamic optimisation of a low speed fan cascade based on genetic algorithm is presented. A commercial turbo machinery CFD code, CFX-TASCow, was used in the evaluation of the objective function. The optimisation process reduces the total pressure Aerodynamic Optimisation of Industrial Axial Fan Blade O.Lotï ¬ iii Executive Summary Cfae UNIVERSITY fold loss while maintaining the same loading and mass flow rate. This development is related to a significant change in profile curvature in the vicinity of the trailing edge. The flow field inside low speed axial fans characterized by low hub-tip ratio can be highly three-dimensional and particularly complex. As a consequence three-dimensional automated design process was developed to aerodynamically optimise the fan blade geometry taking account of the predicted three dimensional flow. The optimiser employs a genetic algorithm for global optimization purposes and is coupled to the academic Navier-Stokes solver MULTIP. The numerical investigation of the overall performance, efficiency and work-input characteristics of the fan were found to agree well with the previously reported experimental results. The optimization task is accomplished by modifying the blade camber line, lean and sweep while keeping the blade thickness distribution and mass flow rate constant. The optimisation process demonstrated that the fan efficiency can be improved by changing the profile curvature and giving the blade a proper forward sweep. Nevertheless the effect of introducing lean and backward sweep did not improve the fan performance for this particular application. This study demonstrated that the present method offers a promising approach to industrial axial fan designers to help design better machines while contributing to the softening of the design cycle. The results obtained show that the genetic algorithm when coupled to a CFD tool is not only capable of achieving a improvement in the designs of existing axial fan blades effectively but also that they achieve these results with a minimum amount of user expertise.