Dr. Daichi Wada Profile

Dr. Daichi Wada

at Japan Aerospace Exploration Agency

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Proceedings Article | 12 April 2017 Presentation + Paper

Inverse analysis of aerodynamic loads from strain information using structural models and neural networks

Daichi Wada, Yohei Sugimoto

Proceedings Volume 10168, 101680W (2017) https://doi.org/10.1117/12.2258583

KEYWORDS: Aerodynamics, Data modeling, Neural networks, Systems modeling, Finite element methods, Machine learning, Structural monitoring, Fiber optics sensors, Fiber optics, Optical sensing, Aerospace engineering, Structural health monitoring, Sensors

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Aerodynamic loads on aircraft wings are one of the key parameters to be monitored for reliable and effective aircraft operations and management. Flight data of the aerodynamic loads would be used onboard to control the aircraft and accumulated data would be used for the condition-based maintenance and the feedback for the fatigue and critical load modeling. The effective sensing techniques such as fiber optic distributed sensing have been developed and demonstrated promising capability of monitoring structural responses, i.e., strains on the surface of the aircraft wings. By using the developed techniques, load identification methods for structural health monitoring are expected to be established. The typical inverse analysis for load identification using strains calculates the loads in a discrete form of concentrated forces, however, the distributed form of the loads is essential for the accurate and reliable estimation of the critical stress at structural parts. In this study, we demonstrate an inverse analysis to identify the distributed loads from measured strain information. The introduced inverse analysis technique calculates aerodynamic loads not in a discrete but in a distributed manner based on a finite element model. In order to verify the technique through numerical simulations, we apply static aerodynamic loads on a flat panel model, and conduct the inverse identification of the load distributions. We take two approaches to build the inverse system between loads and strains. The first one uses structural models and the second one uses neural networks. We compare the performance of the two approaches, and discuss the effect of the amount of the strain sensing information.

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