“…In this section, a range of aerofoils, including high-lift low Reynolds number aerofoils (FX 63-137, S1223, E216) ( 21 ) , supercritical aerofoils (NASA_SC20714, RAE2822, NLR7301) ( 22 , 23 ) , laminar aerofoils (NACA6412, NLF0416) ( 7 , 9 , 24 ) , sailplane aerofoils (AG16, E432) ( 25 , 26 ) , and low-speed aerofoil (SA7035) ( 27 ) , have been employed to test the inverse fitting performance of the proposed NEW CST, the original CST and the LEM CST methods.…”
Section: Geometric Inverse Fitting Test and Resultsmentioning
confidence: 99%
“…AG aerofoils were designed by Dr Mark Drela from MIT. SA aerofoils were improved from SD aerofoils ( 25 – 27 ) . The test results are presented in Figs 23–25.Figure 23Geometric fitting for AG16 using the original CST, LEM CST and NEW CST.Figure 24Geometric fitting for EPPLER432 using the original CST, LEM CST and NEW CST.Figure 25Geometric fitting for SA7035 using the original CST, LEM CST and NEW CST.…”
Section: Geometric Inverse Fitting Test and Resultsmentioning
confidence: 99%
“…AG aerofoils were designed by Dr Mark Drela from MIT. SA aerofoils were improved from SD aerofoils (25)(26)(27) . The test results are presented in Figs 23-25.…”
Section: Case Studies On the Leading Edge And Trailing Edgementioning
A new aerofoil parameterisation method is put forward to represent an aerofoil by combining the leading edge modification class/shape function transformation (LEM CST) method and improved Hicks–Henne bump function’s method. The new class/shape function transformation (NEW CST) method has two additional basis functions comparing the original CST method. In order to confirm these two basis functions, the radial basis functions neural network (RBF) model is trained by some samples which are generated by the Latin hypercube design (LHD) method and Genetic Algorithm (GA) is proposed to achieve the basis functions of the NEW CST method. The NEW CST method has been evaluated in fitting precision of 1,545 aerofoils by comparison with the LEM CST method and the original CST method. And the improved ability of the NEW CST at the leading edge and trailing edge is verified by a series of complex aerofoil case studies within 1,545 aerofoils. The results indicate that the NEW CST method can represent the whole aerofoils and possesses the intuitive property as well as the original CST. Moreover, the number of control parameters (NCP) to parameterise aerofoils is the fewest among these three methods. Furthermore, when the NCP of the NEW CST and LEM CST is the same, the NEW CST method has the higher accuracy and smaller root mean square errors (RMSE) especially at the leading edge and trailing edge.
“…In this section, a range of aerofoils, including high-lift low Reynolds number aerofoils (FX 63-137, S1223, E216) ( 21 ) , supercritical aerofoils (NASA_SC20714, RAE2822, NLR7301) ( 22 , 23 ) , laminar aerofoils (NACA6412, NLF0416) ( 7 , 9 , 24 ) , sailplane aerofoils (AG16, E432) ( 25 , 26 ) , and low-speed aerofoil (SA7035) ( 27 ) , have been employed to test the inverse fitting performance of the proposed NEW CST, the original CST and the LEM CST methods.…”
Section: Geometric Inverse Fitting Test and Resultsmentioning
confidence: 99%
“…AG aerofoils were designed by Dr Mark Drela from MIT. SA aerofoils were improved from SD aerofoils ( 25 – 27 ) . The test results are presented in Figs 23–25.Figure 23Geometric fitting for AG16 using the original CST, LEM CST and NEW CST.Figure 24Geometric fitting for EPPLER432 using the original CST, LEM CST and NEW CST.Figure 25Geometric fitting for SA7035 using the original CST, LEM CST and NEW CST.…”
Section: Geometric Inverse Fitting Test and Resultsmentioning
confidence: 99%
“…AG aerofoils were designed by Dr Mark Drela from MIT. SA aerofoils were improved from SD aerofoils (25)(26)(27) . The test results are presented in Figs 23-25.…”
Section: Case Studies On the Leading Edge And Trailing Edgementioning
A new aerofoil parameterisation method is put forward to represent an aerofoil by combining the leading edge modification class/shape function transformation (LEM CST) method and improved Hicks–Henne bump function’s method. The new class/shape function transformation (NEW CST) method has two additional basis functions comparing the original CST method. In order to confirm these two basis functions, the radial basis functions neural network (RBF) model is trained by some samples which are generated by the Latin hypercube design (LHD) method and Genetic Algorithm (GA) is proposed to achieve the basis functions of the NEW CST method. The NEW CST method has been evaluated in fitting precision of 1,545 aerofoils by comparison with the LEM CST method and the original CST method. And the improved ability of the NEW CST at the leading edge and trailing edge is verified by a series of complex aerofoil case studies within 1,545 aerofoils. The results indicate that the NEW CST method can represent the whole aerofoils and possesses the intuitive property as well as the original CST. Moreover, the number of control parameters (NCP) to parameterise aerofoils is the fewest among these three methods. Furthermore, when the NCP of the NEW CST and LEM CST is the same, the NEW CST method has the higher accuracy and smaller root mean square errors (RMSE) especially at the leading edge and trailing edge.
“…This study uses a fixed-wing RC aircraft with an airfoil Cal2263m [29], root chord wing of 189 mm, tip chord wing of 136 mm, and span of 518 mm [30]. The winglet geometry under review (Figure 1) is winglet offset and cant angle with winglet chord tip measuring 0.21Ct (tip chord) [6] and winglet height 0.5Ct (tip chord).…”
Section: Winglet Geometry Design With Xflr5mentioning
Incorporating winglets into aircraft has been empirically proven to notably improve aerodynamic efficiency by reducing vortex-induced effects at the wingtips. However, conducting comprehensive investigations necessitates the exploration of numerous winglet factors and value variations. This study pertains to remote control aircraft winglets, focusing on manipulating cant angle and offset factors across four distinct values. Two primary objectives guide this research: firstly, the maximization Cl/Cdmax, and secondly, the minimization Cd₀. The Taguchi experimental design is employed to randomize the variations in offset and cant angle values systematically. These variations are then used to generate pivotal regression values in the subsequent particle swarm optimization (PSO). The variance analysis evaluates the impact of winglet-related variables on each research goal. Additionally, the winglet design incorporates the open-source XFLR5 software, an accessible resource for aeromodelling clubs in Indonesia. XFLR5 enables the investigation of aerodynamic parameters across various angles of incidence and plays a crucial role in this research. The results of this study reveal that the Taguchi method yields two distinct combinations of factor values, aligning with the two primary research objectives. Conversely, particle swarm optimization generates a combination that effectively addresses both objectives. A comparative analysis of the winglet factor combinations from Taguchi and PSO underscores the greater efficiency of the PSO method in optimizing winglet variations for two distinct objectives.
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