An Automated Optimal Design of a Fan Blade Using an Integrated CFD/MDO Computer Environment

The objective of the investigation is the development of more efficient design methodologies based on the applications of established design tools including Computational Fluid Dynamics (CFD) and non-linear Multidisciplinary Design Optimization (MDO) algorithms. Well known evolutionary type optimization algorithms include the Particle Swarm Optimization (PSO), Response Surface Optimization (RSO) and Genetic (GA) Algorithms. The benchmark case study is the optimal design of a low speed fan for an industrial air-conditioning application using the Response Surface Optimization (RSO) algorithm. The optimization algorithm controls the variations of parameters that describe the three-dimensional geometry of the blade while applying performance and geometrical constraints on blade shapes that are investigated. The optimal design is defined as the blade geometry which produces the maximum total efficiency subject to specified constraints on the volume flow rate (CFM) and rotational rate (RPM) of the fan