Shape Optimization of a Multi-Element Airfoil Using CFD

Abstract

Movable flap with a NACA airfoil serves as a common control surface for underwater marine vehicles. To augment the functionality of the control surface, a Tab-Assisted Control (TAC) surface was experimentally tested to address its benefits to various different requirements of the control surface. The advantage of the TAC surface could be further enhanced with Shape Memory Alloy (SMA) actuators to control the rear portion of the control surface to form a flexible tab (or FlexTAC) surface. Although the measured FlexTAC data demonstrated similar augmentation in enhancing airfoil’s functionality, they also show subtle differences in data obtained from the TAC and FlexTAC measurements. High fidelity hybrid unstructured RANS calculation results are used to define the flow fields associated with the multi-element FlexTAC airfoil with a stabilizer, a flap and a flexible tab. The prediction results are compared with the measured data obtained from both the TAC and the FlexTAC experiments. The comparison also leads to the resolution of the difference existed between the two data sets. In addition the RANS solutions are validated for predicting the forces and moments acting on the hydrofoil with adequate accuracy for use with an optimization scheme. For a horizontal control surface to effectively provide upward and downward motions, it is necessary to maintain a symmetric airfoil shape. In order to achieve maximum benefit out of a horizontal TAC/FlexTAC surface, a shape modification of the stabilizer (fixed portion of the hydrofoil) and the flap is desirable to account for the requirements at the most severe scenario. This paper focuses on the conditions when the movable flap surface becomes jammed. Since the present investigation deals with a FlexTAC configuration with a flexible tab, the shape modification focuses only on the stabilizer and the non-flexible portions of the flap. The shape optimization calculations coupling with the RANS predictions use an evolutionary algorithm, which consists of a genetic algorithm based design optimization procedure. This procedure searches the complex design landscape in an efficient and parallel manner. Furthermore, it can easily handle complexities in constraints and objectives and is disinclined to get trapped in local extreme regions. The utilization of the hybrid unstructured methodology provides flexibility in incorporating large changes in shape. The mesh regeneration is carried out in an automated manner through a scripting process within the grid generator. The optimization calculation is performed simultaneously on both the stabilizer and the flap. Shape changes to the trailing edge of the stabilizer strongly influence the secondary flow patterns that set up in the gap region between the stabilizer and the flap. These are found to have a profound influence on force and moment characteristics. Experimental and numerical evaluations of a shape obtained from a study of optimization results on the Pareto front for the current optimization landscape, further confirmed the optimization objectives.