Rapid aerodynamic approximation of rotating blades using AI and automation logic

Abstract

The aerodynamic performance of rotorcraft blades critically impacts rotor system efficiency, directly influencing lift, fuel consumption, and aircraft endurance. Conventional fixed-blade designs constrain aerodynamic optimisation across varying flight conditions. Refining techniques applied to rotational aerodynamics presents significant challenges: (a) high complexity, (b) time consumption, and (c) susceptibility to errors. In parallel with advances in non-conventional rotor blade designs, artificial intelligence (AI) has emerged as a transformative technology in aerodynamic modelling, offering enhanced computational capabilities and efficiency. This paper demonstrates how integrating AI-driven aerodynamic modelling facilitates rapid approximation of performance parameters. Specifically, the study addresses three objectives: 1) streamlining methodology while maintaining accuracy, 2) substantially reducing calculation time, and 3) minimising or eliminating errors in manual processing. A Python-based Automation Logic (PAL) algorithm is employed to automate estimation of aerodynamic parameters, reducing reliance on iterative, labour-intensive techniques. Processing time decreased from approximately 200 hours to under 7; a 97% reduction, while preserving computational fidelity and eliminating the ~1.4% rounding error found in manual integration. The findings underscore the transformative potential of AI-driven methodologies in rotorcraft aerodynamics, enabling faster, more reliable, and computationally efficient analyses. Ultimately, the study illustrates how accuracy, speed, and innovation can coexist rather than be mutually exclusive.