Cyclograms as a Novel Analysis Method for Aircraft Handling Qualities Evaluations

This paper establishes Cyclograms as a novel rainflow-counting-based method for analyzing aircraft data for the purpose of handling qualities evaluations. Conventional metrics such as Adequate and Desired performance percentages provide useful compliance measures but fail to capture the dynamic structure of tracking errors. The proposed approach leverages rainflow algorithms to transform time-domain tracking errors into structured cycle-based representations, enabling visualization of the underlying tracking error structure. Using archival flight test data from a variable-stability aircraft, sum-of-sines tracking tasks were performed under baseline, failure, and adaptive cueing configurations. Using the data set consisting of over 90 rated runs, Cyclogram-derived metrics were evaluated for statistical significance. Principal Component Analysis and correlation studies reveal strong alignment between Cyclogram metrics and traditional measures, with Cyclogram based metrics exhibiting the highest correlation to awarded Handling Qualities Ratings (HQRs). Results demonstrate that Cyclograms provide scalable visualization, capture dynamic performance trends, and offer robust quantitative indicators consistent with Cooper-Harper ratings. These findings support Cyclograms as a practical addition to the handling qualities evaluation toolkit.