A fuzzy genetic approach for velocity estimation in wind-tunnel

Schlieren imaging in wind-tunnels is extensively utilized to study the effects of air on a moving object. One of the interested subjects for research is to study the effects of speed change on the object surface. Speed change results in the occurrence of shock waves, which are visualized as lines on Schlieren images. However, computing new relevant velocity of the wind-tunnel requires solving sophisticated and time-consuming formulas. In this paper, we investigate the problem of estimating relevant speed of the object after occurrence of a shock wave. At first, we propose a feature set of the image that are influenced by the shock wave. Therefore, these features are extracted by the developed image processing component. Afterward, we propose a fuzzy genetic algorithm to estimate the new velocity of the object. We make use of the genetic algorithm to tune the membership functions of the variables of the fuzzy system by leveraging some training images. The evaluation is performed by computing the accuracy of the velocity estimation. For this, the proposed fuzzy system runs by the extracted features of these images and estimates the new velocity. The comparison of the estimated with the real values shows a very close and accurate estimation.