Optimizing Elliptical Tank Shape Based on Real-Coded Genetic Algorithm

An elliptical tank cross-section is formulated to explore an optimization method, based on a real-coded genetic algorithm to enhance the roll stability limit of a tank vehicle. The genetic algorithm shape optimization problem is applied to minimize the overturning moment imposed on the vehicle due to CG height of the liquid load, and lateral acceleration and cargo load shift. The minimization process is performed under some major constraints such as cross-sectional area, overall height and width. The magnitudes of lateral and vertical translation of the cargo within the proposed optimal cross section under a constant lateral acceleration field are compared with those obtained with currently used elliptical cross-sections to demonstrate the performance potentials of the optimal shapes. The comparison of vehicle overturning moment revealed that the proposed optimal tank geometry is approximately 12% higher than the tank vehicle equipped with currently used elliptical and circular cross section tanks.