Detailed Study of Photogrammetry Technique as a Valid Ice Accretion Measurement Method

<div class="section abstract"><div class="htmlview paragraph">Aircraft icing is an important subject for investigation due to its critical effects on flight performance. Ice accretion analysis is commonly carried out using computational tools, from which parameters such as the mean ice shape and roughness characteristics can be obtained, as these parameters have a strong effect on the physics of aerodynamics and ice accretion. Hence, the accurate digitization of a generated ice shape through ice measurement techniques is of crucial importance. This study aimed to validate the use of photogrammetry for measurement of ice geometries and roughness on UAV airfoils, by comparing it with the cast-and-mold method. Two test cases, one mixed and second rime ice, were analyzed, each case with three subcases varying in the number of photographs used. For test case 1, mixed ice, photogrammetry method resulted in an underestimation of mean ice height by 0.5 mm in the smooth zone and overestimation by 0.2 mm and 0.6 mm on the pressure and suction sides, respectively, in the rough zone with feathers compared to the 3D-scan of the mold. The absolute surface roughness error amongst the 3 datasets was <b><i>±</i></b>0.1 mm. Results indicated that the subcase with the most photographs had the least amount of ice geometry errors, but the impact of number of images on the surface roughness was negligible. In test case 2, rime ice, the results showed that even with a smaller number of photos, surface roughness was captured well, given the base geometrical noise roughness to be 0.03 mm. These findings indicate that good predictions of surface roughness can be made with a small number of photos for rime ice surfaces. Possible sources of error in capturing ice geometry include lighting, shadows, camera angles, and software reconstruction errors. The addition of global control points on the stagnation line can provide a better reference for the reconstruction software and reduce error. Accuracy of surface roughness can be improved by reducing the base geometrical noise, which can be achieved with a painting technique using a smaller droplet distribution. In conclusion, photogrammetry is a viable alternative for measurement of surface roughness on UAV airfoils.</div></div>