Published on September 27, 2018
1. A Computational and Experimental Study of the Aerodynamics of a Micro Air Vehicle Erin Laura Williams
2. Motivation • A Micro Air Vehicle (MAV) is defined to: • have dimensions between 0.15 m and 1.00 m • weigh between 50 g and 2 kg • Many potential applications of MAVs • Civil: natural disaster recovery/surveillance • Military: surveillance including inside buildings and around urban warzones • can be equipped with sensors to detect chemicals or heat • can be used in swarms when multiple are required • Applications are hindered by little information known about low Reynolds number flight physics (Re ≤ 150,000) Hassanalian, M., Abdelkefi, A., (2017). Classifications, applications, and design challenges of drones: A review. Progress in Aerospace Sciences [online]. 91(1), 99-131. [Accessed 22nd February 2018]. Available from: doi: 10.1016/j.paerosci.2017.04.003
3. Aims & Objectives • Validate a time-dependent computational fluid dynamics (CFD) simulation against results found through wind tunnel experimentation • Explore the advantages and disadvantages of using time-dependent simulations to capture low Reynolds number flows • Discuss some of the problems faced when conducting wind tunnel experimentation • Research will add to the: • current knowledge of low Reynolds number flight physics • the knowledgebase of cases that have used the IDDES turbulence model
4. Wind Tunnel Model • Current model was damaged and could not be used in the wind tunnel • New model was made using Depron foam, epoxy resin, and a pre-cut Zimmerman planform • To match the CAD model more closely, it was made without: • vortex generators • elevators • tail and rudder • support struts • electronic equipment • Circular metal plate was attached with epoxy underneath the centre of gravity on the base of the fuselage • allows the model to be attached to the sting
5. Wind Tunnel Methodology • Vertical sting • pipe insulation was attached to the sting to ease meshing • A load cell with a resolution of 0.003125 N was used • Tests were run first at 3 m/s and 6 m/s to ensure structural rigidity and to determine the LabView settings • sampling frequency was chosen to collect one result per 5 seconds • Recorded tests were run at 10 m/s for angles between -4° and 14° • Forces measured were post-processed to represent lift and drag forces
6. CFD Mesh • Blocking strategy used by Pratt (2015) was applied to the true wind tunnel domain • Initial simulations diverged due to turbulence not being fully resolved by the outlet • Domain length after the MAV was doubled • Mesh independence was completed using lift coefficient variable • Final mesh had around 1.6 million elements with good mesh metrics despite being a complex geometry Pratt, T., (2015). Vane-type vortex generators for the increased CL and enhanced stability of a Zimmerman-wing Micro Air Vehicle at low Reynolds numbers. MSc(Res) Dissertation, University of Sheffield.
7. CFD Methodology • Transient IDDES k-ω SST turbulence model • Pressure-based solver • Velocity-inlet, symmetry, and outflow boundary conditions • CFL criterion was used to calculate the time-step of 2.4 × 10−6 seconds • Iterations per time step were decreased with number of time steps • Data was collected for 1 second of flow time but only one angle could cumulatively converge within this time
8. Flow Visualisation at 0° Angle of Incidence
9. Flow Visualisation at 12° Angle of Incidence
10. Comparison of Results – Coefficient of Lift 0 0.2 0.4 0.6 0.8 1 1.2 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 CoefficientofLift Angle of Incidence (°) Coefficient of Lift vs Angle of Incidence Wind Tunnel Previous RANS Results CFD Simulations Pratt, T., (2015). Vane-type vortex generators for the increased CL and enhanced stability of a Zimmerman-wing Micro Air Vehicle at low Reynolds numbers. MSc(Res) Dissertation, University of Sheffield.
11. Comparison of Results – Coefficient of Drag Pratt, T., (2015). Vane-type vortex generators for the increased CL and enhanced stability of a Zimmerman-wing Micro Air Vehicle at low Reynolds numbers. MSc(Res) Dissertation, University of Sheffield. 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 CoefficientofDrag Angle of Incidence (°) Coefficient of Drag vs Angle of Incidence Wind Tunnel Previous RANS Results CFD Simulations
12. Conclusion and Future Work • Validation can be deemed successful at low angles of incidence, but not at higher angles • Discrepancies are either due to: • the wind tunnel set-up and the errors associated with experimentation • the CFD set-up including the length of time the simulations were run for • a combination of both set-ups • Areas of future work: • mesh the vertical sting geometry and re-run the study over a larger range of angles to compare the difference in results • mesh the horizontal sting geometry and re-run the study to show the difference in the choice of sting • run a length study on the domain to determine the minimum length that can be simulated using the chosen fluent set-up