Conducting eco-hydraulic simulation experiments using embodied intelligent fish

The design and optimization of fishways and other fish-passage facilities are one of the critical issues in hydraulic engineering. Traditional methods using physical experiments for fishway design and optimization face challenges such as the uncontrollability of fish behavior, limited non-intrusive measurement techniques, and nonlinear scale effects. Numerical simulation methods can provide the hydraulic performance information of fishways by analyzing flow velocity, turbulence energy, and flow patterns, but fail to account for the active responses of fish to the hydrodynamic characteristics of the environment. In this study, a research paradigm of using embodied intelligent fish to optimize eco-hydraulic facilities was attempted. The core of this research paradigm is a simulation platform for intelligent fish based on deep reinforcement learning (DRL) and the immersed boundary–lattice Boltzmann (IB-LB) coupling algorithm. Based on this platform, by endowing fish with biological tendencies and biometric features related to perception/feedback/decision-making at the individual scale, embodied intelligent fish can achieve multimodal perception and autonomous decision-making in complex fishway digital flow fields based on the potential habits of live fish. Using the proposed framework, conduct end-to-end deep reinforcement learning training on intelligent fish and then deploy the trained embodied intelligent fish in a virtual vertical-slot fishway for ecological hydrodynamic experiments. Comparative analyses were conducted on the fish-passage performance of three different fishway geometric shapes. The results demonstrated that the new research paradigm evaluates the structural characteristics of fishways through the adaptive response behavior of fish to the hydrodynamic environment, which can provide quantitative guidance for fishway design from the terms of passage path, passage time, energy consumption, etc. This study belonged to an individual-scale digital twin and was an innovative attempt to utilize virtual intelligent lifeforms at the individual scale to conduct simulation-based experimental research.