Dynamic Interaction Mechanism Between Periphytic Algae and Flow in Open Channels

Periphytic algae, as representative aquatic epiphytic communities, play a vital role in the material cycling and energy flow in rivers. Through physiological processes such as photosynthetic carbon fixation and nutrient absorption, they perform essential ecological functions in water self-purification, maintenance of primary productivity, and microhabitat formation. This study investigates the interaction mechanisms between these highly flexible organisms and the hydrodynamic environment, thereby addressing the limitations of traditional hydraulic theories developed for rigid vegetation. By incorporating the coupled effects of biological flexibility and water flow, an innovative nonlinear resistance model with velocity-dependent response is developed. Building upon this model, a coupled governing equation that integrates water flow dynamics, periphytic algae morphology, and layered Reynolds stress is formulated. An analytical solution for the vertical velocity distribution is subsequently derived using analytical methods. Through Particle Image Velocimetry (PIV) measurements conducted under varying flow velocity conditions in an experimental tank, followed by comprehensive error analysis, the accuracy and applicability of the model were verified. The results demonstrate strong agreement between predicted and measured values, with the coefficient of determination R2 greater than 0.94, thereby highlighting the model’s predictive capacity in capturing flow velocity distributions influenced by periphytic algae. The findings provide theoretical support for advancing the understanding of ecological hydrodynamics and establish mechanical and theoretical foundations for river water environment management and vegetation restoration. Future research will build upon the established nonlinear resistance model to investigate the dynamic coupling mechanisms between multi-species periphytic algae communities and turbulence-induced pulsations, aiming to enhance the predictive modelling of biotic–hydrodynamic feedback processes in aquatic ecosystems.