## 6 Experimental Methods for Evaluating HAPS Propeller Performance  
A critical comparison of the optimization strategies discussed in Section 5.2 highlights key trade-offs between fidelity, cost, and reliability. CFD-only approaches, such as those by [PERSON] [51] and [PERSON] [108], capture complex aerodynamic phenomena like stall delay and tip losses at low Reynolds numbers (\(\mathrm{Re}\approx 5\times 10^{4}\) to \(2\times 10^{5}\)), but are sensitive to mesh quality and transition modeling. Efficiency discrepancies of up to 15% were reported when comparing to XFOIL and BEMT models. In contrast, surrogate and multi-fidelity approaches aim to balance accuracy and speed. [PERSON] [104] employed a three-level Hierarchical Kriging model (BEMT, Vortex Theory, RANS) to solve a multi-objective problem with convergence in just 35 iterations. [PERSON] [94] demonstrated a 20-25% increase in propulsive efficiency through high-fidelity optimization with reduced computational effort. However, both methods are fundamentally limited by the scarcity of validation data. Experimental approaches to propeller testing for HAPS are therefore critical for validating aerodynamic predictions and assessing performance in low-density atmospheric conditions. In [108], the results obtained from wind tunnel tests were compared with those derived from low-fidelity (BEMT) and high-fidelity (CFD) methods for a propeller operating at low Reynolds number. The tested propeller was an APC 14 \(\times\) 7e model, made of fiberglass composite, with a diameter of 35.56 cm and a pitch of 17.78 cm. The wind tunnel experiments were conducted at the Experimental Aerodynamics Laboratory of the Federal University of Minas Gerais (Brazil), using a closed-circuit wind tunnel with a test section measuring 1.0 m in width and 1.2 m in height. The propeller was mounted in a tractor configuration and operated at a constant rotational speed of 7900 rpm throughout all tests. The freestream air velocities were varied from 0 to 25 m/s, resulting in advance ratio values ranging from 0 to 0.5. The air density during testing was \(\rho~{}=~{}1.185\) kg/m\({}^{3}\), and the kinematic viscosity was \(\
u~{}=~{}1.4515\times 10^{-5}\) m\({}^{2}\)/s. Turbulence intensity in the test section was maintained at approximately 0.8%, ensuring a uniform and steady flow. Thrust measurements were taken using a G-model load cell with a sensitivity of \(2\pm 10\%\) mV/V and a reading range between 5 kg and 50 kg, with \(\pm 10\) g accuracy. These experimental results were compared to different numerical approaches (Figure 20): BEMT models such as QProp and JBLADE showed good agreement with wind tunnel data at low advance ratios (\(J\leq 0.3\)), where the flow is dominated by low Reynolds numbers and rotational effects. JBLADE, in particular, provided consistent results even at higher \(J\) due to its incorporation of 3D flow effects. Conversely, the CFD-AMI (Arbitrary Mesh Interface) model exhibited superior accuracy at higher advance ratios, benefiting from transient rotor-stator interaction modeling and turbulence resolution, albeit at a significantly higher computational cost. The CFD-FR (Frozen Rotor) model maintained a relatively constant error (\(\sim\)20%) across conditions but predicted efficiency reliably. These comparisons underscore the importance of selecting the appropriate method based on flow regime and computational resources, while also highlighting the critical role of experimental testing in validating and benchmarking both low- and high-fidelity numerical predictions. To further demonstrate how experimental testing supports the development of propellers for HAPSs, another notable study is described in [128]. In particular, a comprehensive experimental evaluation of a propeller designed for the solar-powered HALE UAV CAV-3 was presented.