The transformative potential of machine learning for experiments in fluid mechanics

TL;DR

This paper discusses how machine learning can significantly enhance experimental fluid mechanics by improving measurement accuracy, enabling better experimental design, and facilitating real-time control, highlighting recent successes and future challenges.

Contribution

It provides a comprehensive overview of recent advances and potential future applications of machine learning in experimental fluid dynamics, emphasizing new methodologies and challenges.

Findings

ML improves measurement fidelity in fluid experiments

ML enables real-time estimation and control

ML facilitates the development of digital twin models

Abstract

The field of machine learning has rapidly advanced the state of the art in many fields of science and engineering, including experimental fluid dynamics, which is one of the original big-data disciplines. This perspective will highlight several aspects of experimental fluid mechanics that stand to benefit from progress advances in machine learning, including: 1) augmenting the fidelity and quality of measurement techniques, 2) improving experimental design and surrogate digital-twin models and 3) enabling real-time estimation and control. In each case, we discuss recent success stories and ongoing challenges, along with caveats and limitations, and outline the potential for new avenues of ML-augmented and ML-enabled experimental fluid mechanics.