Robust Optimal Aerodynamic Design Using Evolutionary Methods and Neural Networks
Man Rai · 42nd AIAA Aerospace Sciences Meeting and Exhibit · 2004
Evolutionary algorithms and neural networks have been used successfully in various disciplines of aeronautical engineering including aerodynamic design. Here a new evolutionary method for multiple –objective optimization is presented. It draws upon ideas from several genetic algorithms and evolutionary methods; one of them being a relatively new mem ber to the general class of evolutionary methods called differential evolution. The capabilities of the evolutionary method developed here are investigated using some complex test cases. Good solution accuracy and diversity are obtained in all these cases . Traditionally, aerodynamic shape optimization has focused on obtaining the best design given the requirements and flow conditions. However, the flow conditions are subject to change during operation. It is important to maintain near -optimal performance l evels at these off -design operating conditions. Additionally the accuracy to which the optimal shape is manufactured depends on the available manufacturing technology and other factors such as manufacturing cost. It is imperative that the performance of th e optimal design is retained when the component shape differs from the optimal shape due to manufacturing tolerances and normal wear and tear. These requirements naturally lead to the idea of robust optimal design wherein the concept of robustness to vario us perturbations is built into the design optimization procedure. Here we demonstrate how both evolutionary algorithms and neural networks can be used to achieve robust optimal designs. Test cases include the design of airfoils and, fins used in boiling he at transfer.