Machine learning for flow field measurements: a perspective
Stefano Discetti, Yingzheng Liu
Abstract
Abstract Advancements in machine-learning (ML) techniques are driving a paradigm shift in image processing. Flow diagnostics with optical techniques is not an exception. Considering the existing and foreseeable disruptive developments in flow field measurement techniques, we elaborate this perspective, particularly focused to the field of particle image velocimetry. The driving forces for the advancements in ML methods for flow field measurements in recent years are reviewed in terms of image preprocessing, data treatment and conditioning. Finally, possible routes for further developments are highlighted.
Topics & Concepts
Perspective (graphical)Particle image velocimetryComputer sciencePreprocessorField (mathematics)Flow (mathematics)VelocimetryOptical flowArtificial intelligenceImage processingComputer visionImage (mathematics)OpticsPhysicsMechanicsMathematicsTurbulencePure mathematicsFluid Dynamics and Turbulent FlowsPlant Water Relations and Carbon DynamicsHydrology and Sediment Transport Processes