Solar car racing has created a competitive platform for research into alternative energies, particularly the utilization of solar energy. This paper reports on a numerical optimization of the vehicle body shape, utilizing Computational Fluid Dynamics (CFD). Optimization and validation of the body shape, fairing position, body-and-fairing fillet blend, fairing leading edge curvature, driver position and canopy design were considered. For the purposes of this study, the fairing and driver position optimization will be discussed as a case study to illustrate the design and optimization methodology with the improved product of area and the coefficient of drag (ACd) values recorded. The algorithms developed and procedures followed to adapt the design of the vehicle are presented. The algorithms applied relied on comparing designs based on evaluating shape function curves representing the normalized (body length) sectional area of the body under consideration. With the aid of numerical analysis software and applying the design optimization algorithm by evaluating ACd, it was possible to optimize the shape of the main wing and the placement of the fairings and the driver compartment/canopy. The results of the CFD simulation showed there was a direct correlation between the drag coefficient and the shape function. By utilizing this methodology, significant improvements to the coefficient of drag could be realized. A reduction of 31% in the coefficient of drag was achieved by moving the centered driver position to the side. This design change then also contributed to an energy saving of 442.6 Watt at a speed of 100km/h.

Solar car racing has created a competitive platform for research into alternative energy solutions and aids development in the green engineering space. The University of Johannesburg’s Solar Racing team developed a vehicle (Ilanga II) to compete in the 2014 South African Solar Car Challenge. This paper describes the numerical optimization of the vehicle’s body shape, utilizing Computational Fluid Dynamics (CFD) and finally compares the simulated results with the actual performance during the race. Motor control data is used to determine the aerodynamic drag coefficient of the vehicle. This work builds on the paper submitted in 2014 [1], which postulated the use of the Hermite cubic function in conjunction with the shape function analysis as a holistic design tool. By analyzing the motor control data it is possible to comment on the effectiveness of the shape function analysis technique. The final optimized design predicted a straight-line ACd 0.078. A yaw angle characterization study of ±25° degrees, in conjunction with historic weather data were used to fully characterize the vehicle with an average drag area coefficient of 0.119. The final comparative results of the simulated data and the race data show that the vehicle’s straight-line (Zero yaw) ACd was 11.2% higher than the simulated results, whereas the average aerodynamic characteristic ACd was 2.43% lower than the simulated results.

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