Session: 01-08-02 Digitalization, AI/ML, Digital Twins - II

Submission Number: 156996

Inferring Structural Motion and Properties in Fluid Through Wake Flow Sensing 

Understanding the dynamics and properties of structures moving in fluid flows is crucial for applications in underwater robotics, offshore systems, and marine sensing. As an object moves in the fluid with a Reynolds number above 10, periodic vortex shedding can form, creating unique wake flow signatures. This work explores a hybrid framework for inferring structural motion and properties by analyzing wake flow signatures generated by structures in the fluid. By leveraging machine learning techniques that are rotation- and translation-invariant, the proposed approach decouples the orientation and translation of the structure from other latent features that influence the wake flow signatures. These latent features are then used to infer the structural properties via a separate neural network model. To evaluate the effectiveness of the approach, we generate diverse wake flow signatures from bluff bodies and fish locomotion using computational fluid dynamics models. Despite the high-dimensional variability of wake patterns, the machine learning models identify a few key nonlinear features that describe the variations. These features are found to be distinctive for different structural motions and properties in the fluid. We demonstrate the success of this hybrid framework and highlight its advantages over traditional inference methods. The developed inference model for wake flow sensing can add a new modality to monitor the states of underwater systems. 

Presenting Author: Leixin Ma Arizona State University

Presenting Author Biography: Leixin Ma is an assistant professor in the Mechanical and Aerospace Engineering program at Arizona State University. She was a postdoctoral fellow in the Department of Mechanical and Aerospace Engineering at UCLA. She was also a senior research personnel at UCLA Clean Energy Smart Manufacturing Innovation Institute. She received her BSc in Naval Architecture & Ocean Engineering from Shanghai Jiao Tong University in 2015, an S.M. degree, and a Ph.D. degree in mechanical engineering from MIT in 2017 and 2021, respectively. Her research interest is in the physics-constrained data-driven approach to studying fluid-structure interaction and mechanics problems. She was awarded the Ho-Ching and Han-Ching Fund Award from the MIT Office of Graduate Education in 2019. She also served as a Teaching Development Fellow at MIT from 2020-2021.