Optimal trajectories for Bayesian olfactory search in turbulent flows: The low information limit and beyond.

ABSTRACT: In turbulent flows, tracking the source of a passive scalar cue requires exploiting the limited information that can be gleaned from rare, randomized encounters with the cue. When crafting a search policy, the most challenging and important decision is what to do in the absence of an encounter. In this work, we perform high-fidelity direct numerical simulations of a turbulent flow with a stationary source of tracer particles, and obtain quasi-optimal policies (in the sense of minimal average search time) with respect to the empirical encounter statistics [1-3]. We study the trajectories under such policies and compare the results to those of the infotaxis heuristic. In the presence of a strong mean wind, the optimal motion in the absence of an encounter is zigzagging (akin to the well-know

SUBMITTER: Heinonen RA 

PROVIDER: S-EPMC12369886 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature