ML-Based Wing Shape, Loads, and AoA Sensing from measured strain: From Ground Test to Flight-Ready Integration

The research employs a practical approach, utilizing relatively simple static ground tests with a few concentrated loads to train the ML model, which subsequently predicts aerodynamic distributed loads and wing deformation during wind tunnel tests.
A comprehensive workflow for designing training experiments and evaluating results is detailed, complemented by a well-structured data processing and training pipeline. The concept is validated using ML models initially trained on a finite element (FE) structural model of the wing, simulating loads that produce geometrically nonlinear deflections up to 50% (275mm) of the wing’s 550mm span.
The performance of the load and shape prediction models is then evaluated using actual wind tunnel test data with deflections up to 100mm, achieving maximum errors below 9.1%. The generalization and extrapolation capabilities of the models are assessed and demonstrate robust performance beyond the training domain.
Finally, the ML models are employed to determine the wing’s aerodynamic characteristics and estimate the rigid AoA in wind tunnel tests ranging from -10° to 10°, with a maximum error of 5.2% (0.6°) within the model’s training range.
This study successfully establishes a comprehensive methodology for predicting in-flight shape and loads using ground-test-trained ML models, providing an effective approach for estimating RAoA for highly flexible structures.

Ido is a master’s student at the Faculty of Aerospace Engineering at the Technion, under the supervision of Prof. Daniela Raveh. He holds a bachelor’s degree in Mechanical Engineering from the Technion and has over a decade of experience in the aerospace industry. His areas of expertise include finite element analysis (FEA), multibody dynamics, experimental testing, and machine learning. Ido focuses on integrating simulation, experimentation, and advanced algorithms to bridge the gap between physics-based modeling and artificial intelligence, with the goal of enhancing engineering and validation processes for advanced airborne structures.