An implementation of individual-based fish model in OpenFOAM

Individual-based models (IBMs) are widely used to represent individuals of aquatic organisms, for example for the study of fish populations, fish movement, and species interactions in rivers and streams. The main idea is to estimate population dynamics through the movements of individuals, which are in response to hydrodynamic cues, i.e. velocity, pressure, and other flow variables. Thus, the application of IBMs first requires spatial and temporal resolutions of the fully-developed flow field, for example, using computational fluid dynamics (CFD) tools. Then, an IBM model is fed with the CFD results to predict individual movement in response to hydrodynamics. In this work, one kind of IBM, the Eulerian-Lagrangian agent model (ELAM), was implemented in the open source CFD platform OpenFOAM. In the ELAM model, a 3D CFD simulation can be run first in the Eulerian framework using the numerous ready-to-use CFD solvers in the OpenFOAM. Then each aquatic organism is modeled as a Lagrangian particle and its movement follows the behavior rules programmed in the agent framework. The programmed behavior rules are based on knowledge of fish biology for a given species. Then, the robust Lagrangian particle tracking module in OpenFOAM can calculate and update the particle’s new positions according to the programmed behavior rules. The Lagrangian particle tracking in OpenFOAM uses a barycentric tracking algorithm, which makes the conversion between Eulerian and Lagrangian frameworks efficient and reliable. The details of the implementation of ELAM in the OpenFOAM will be presented. Then, a preliminary application run will be shown for estimating the fish movement trajectory in a fish passage.