Sailplane fuselage aerodynamic optimization using CFD

Abstract

In sailplane aerodynamic design, the foremost validation technique applied is computational fluid dynamics (CFD) simulation due to its ability to obtain accurate aerodynamic information without the need for a wind tunnel. Although the simulation of a sailplane in order to determine aerodynamic parameters such as lift and drag is simple, extensive design experience is required to improve these parameters. CFD based shape optimization techniques have been applied on two-dimensional (2D) streamlined shapes such as airfoils, but due to the complexities involved in three-dimensional (3D) transitional flow on streamlined surfaces, it has not yet been applied to sailplane fuselage design. CFD packages such as Simcenter STAR-CCM+ and Ansys® Fluent, uses adjoint technology to determine mesh sensitivity with respect to design parameters. With this information available, a mesh morphing tool is applied and the geometry is deformed to obtain a more optimized shape. Several shape optimizations with the adjoint technology have been documented, ranging from the pressure drop in thin walled pipes, downforce on the front wing of a race car and in one case even the lift-to-drag ratio of a sailplane model. In all of these cases the Spalart-Almaras turbulence model was applied. The Spalart-Almaras turbulence model is unable to predict boundary layer transition positions, which is crucial for accurate sailplane simulations. The Spalart-Almaras turbulence model can therefore not be applied in the optimization method. With drag on a sailplane fuselage existing mainly of pressure and skin friction drag, the optimization method used in this study would have to reduce the wetted surface area by contracting the fuselage behind the cockpit, whilst maintaining a positive pressure gradient to avoid or delay boundary layer separation. By substituting the Spalart-Almaras turbulence model used in the adjoint method by Siemens (2019), with the SST 𝑘 − 𝜔 turbulence and 𝛾 − 𝑅𝑒𝜃 transition models, a new optimization method was formed. The new method was validated on a simple 3D geometry in the shape of a bullet. The bullet shape had a blunt tail and was simulated to consist of laminar and turbulent boundary layer flow. A 35% drag reduction was obtained by modifying the rear of the bullet shape to represent a boat tail. Points allocated at different positions on the geometry are used by the mesh morpher to alter the mesh, and different allocation of these points can produce varying results. From seven simulations of different point setups performed on the simple geometry, three produced a drag reduction, of which two were implemented on the baseline model. With the positive results obtained on the simple geometry, the method was applied on a two seater sailplane fuselage, adopted from scale model drawings of the Schempp-Hirth Arcus. The frontal fuselage and tail were kept constant so as to preserve cockpit size and boom thickness. The design of the wing-fuselage junction was not addressed in this paper, but due to the importance of its flow effects on the fuselage design, a simple untapered wing was added to the fuselage. The wing and fuselage combination formed the baseline model. After several optimization attempts on the baseline model, it was discovered that the simulation convergence level was insufficient, although a convergence level precise to two decimal places was achieved. The 17 million cell mesh, determined by the mesh independence study to be sufficiently refined, was not adequate for application in the adjoint method. The mesh was then further refined to 87 million cells without achieving a higher convergence level. It was then discovered that the complexities of the prims layer mesh at the wing-fuselage junction was responsible for the convergence issue. Due to the design of the wing-fuselage junction being beyond the research limitations, the wing was removed from the geometry. The area in the immediate vicinity of the wing-fuselage junction was kept constant in an attempt to minimize the effects of its omission. The omission of the wing resulted in a solution with a convergence level precise to four decimal places, which proved to be sufficient. The wingless baseline model was optimized and a 2.8% drag reduction was achieved, which when translated to total sailplane drag reduction, equates to a 1.0% drag reduction. The 1.0% total sailplane drag reduction was nearly sufficient to justify a sailplane manufacturer introducing a new model to its range. Although an optimal fuselage shape was not achieved, drag reduction with the adjoint solver in STAR-CCM+, using the SST 𝑘 − 𝜔 turbulence and 𝛾 − 𝑅𝑒𝜃 transition models, was possible. The use of the adjoint solver as CFD optimization method paves the way for future improvements in the aerodynamic design of high performance sailplanes.