سال انتشار: ۱۳۸۸
محل انتشار: دوازدهمین کنفرانس دینامیک شاره ها
تعداد صفحات: ۹
R Shafaghat – PhD Student Faculty of Mechanical Engineering Iran University of Science and Technology
S.M Hoseinalipour – Associate Professor Faculty of Mechanical Engineering Iran University of Science and Technology
I Lashgari – MSc Student Faculty of Mechanical Engineering Iran University of Science and Technology
R Ebrahimi – Associate Professor Faculty of Aerospace Engineering K. N. Toosi University of Technology
The reduction of energy consumption of high speed submersible bodies is an important challenge inhydrodynamic researches. In this paper the effects of cavity length on cavitator optimum shape in supercavitating flows is studied. An axisymmetric supercavitation potential flow passes an axisymmetric cavitator, which is placed perpendicular to the flow and immediately a cavity is formed behind the cavitator. This is because of the generation of a gas or vapor cavity between the body and the surrounding liquid due to the change in a high speed flow direction passing the cavitator. Drag force acting on this supercavitating body dictates the thrust requirements for the propulsion system, to maintain a required cavity at the operating speed. Therefore, any reduction in the drag force, by modifying the shape of the cavitator, will lead to decrease this force. This study is concentrated on the optimization of axisymmetric cavitators in order to decrease drag coefficient for the variable cavity length. Drag Coefficients are determined, using supercavitation software that is developed by authors. According to this point of view, after determination of cavitator geometric parameters, the effects of variable cavity length on optimum cavitator geometric parameters are presented. To achieve this goal a multi-objective optimization problem is defined to optimize cavitator shapes in supercavitating flow. The so called NSGA II (Non dominated Sorting Genetic Algorithm) algorithm is used as an optimization method.
Design parameters and constraints are obtained according to supercavitating flow characteristics and cavitator modeling and objective functions are generated using Linear Regression Method.