Publication Type : Conference Proceedings
Publisher : Elsevier BV
Source : Materials Today: Proceedings
Url : https://doi.org/10.1016/j.matpr.2023.09.197
Keywords : Unmanned air vehicles, Lift coefficient, Drag coefficient, Lift-to-drag ratio, NACA airfoils, Finite element method
Campus : Bengaluru
School : School of Artificial Intelligence
Year : 2023
Abstract : The current article delineates the importance of the aerodynamic characteristics of airfoils utilized in unmanned aerial vehicles (UAVs) and their evaluation. UAVs are excessively in demand for the major use of medical applications in the present situations where unexpected climatic changes occur. The increasing demand for UAVs in medical applications like healthcare, transporting medicines to remote areas of medical necessity, and surveillance of affected areas and disastrous sites having biological hazards. The prerequisite transportation of essential medical help for the individuals or groups of crucial areas such as those affected by floods, drought, or other natural calamities can be efficiently carried out by UAVs. There is currently a significant demand for airfoils that can enhance the lift and improve the efficiency of UAVs used in medical applications. The finite element meshing technique is valuable in accurately assessing the aerodynamic properties and selecting suitable airfoils for UAVs. In this regard, the NACA 0009 and NACA 6409 airfoils are frequently utilized, and their lift coefficient, drag coefficient, and lift-to-drag ratio have been evaluated at low Reynolds numbers. To ensure precise evaluation, an innovative subparametric higher-order meshing technique has been employed for these specific airfoils. It has shown a tremendously better result with the higher order meshing techniques.
Cite this Research Publication : K. Chandan, Supriya Devi, K.V. Nagaraja, Effective evaluation of aerodynamic characteristics using subparametric finite element transformation for unmanned air vehicles at low Reynolds number, Materials Today: Proceedings, Elsevier BV, 2023, https://doi.org/10.1016/j.matpr.2023.09.197