Detecting flow events in turbulent flow of vertical-slot fish passes

Abstract

According to earlier research, one key features determining the influence of turbulence on fish swimming behavior is the short-term predictability of turbulence that, in this study, is interpreted as the existence of turbulent flow events in the flow field whose occurrences are predictable. Here, the fundamental assumption is that repeating sequences of flow events are the basis for predictability, because repetition facilitates their recognition by experience on the fish’ part and, therefore, flow events occurring later in time within the repeating sequences are considered predictable. This thesis presents a flow-analysis methodology for detecting repeating sequences of large-scale turbulent flow events and their occurrences in the flow. Here, the term flow event refers to changes in the flow field associated with the appearance of turbulent flow structures or with their change of position, while the term occurrence denotes instances of times at which the particular flow event happens. The presented methodology is applied in this work to a Particle Image Velocimetry measurement performed in the scale model of a vertical-slot fish pass and uses Proper Orthogonal Decomposition (POD) in a novel way. First, large-scale repeating flow events represented by POD modes are identified, then, the sequence of their occurrences is detected from the time series of the POD coefficients. This methodology has also a potential to be applied to sparse point-measurement techniques that are suited for fish-behavior experiments in full-scale facilities.