Exploring Bayesian olfactory search in realistic turbulent flows

The problem of tracking the source of a passive scalar in a turbulent flow is relevant to flying insect behavior and several other applications. Extensive previous work has shown that certain Bayesian strategies, such as "infotaxis," can be very effective for this difficult "olfactory search" problem. More recently, a quasioptimal Bayesian strategy was computed under the assumption that encounters with the scalar are independent. However, the Bayesian approach has not been adequately studied in realistic flows which exhibit spatiotemporal correlations. In this work, we perform direct numerical simulations (DNSs) of an incompressible flow at Re-lambda similar or equal to 150, while tracking Lagrangian particles that are emitted by a point source and imposing a uniform mean flow with several magnitudes (including zero). We extract the spatially dependent statistics of encounters with the particles, which we use to build Bayesian policies, including generalized ("space-aware") infotactic heuristics and quasioptimal policies. We then assess the relative performance of these policies when they are used to search using scalar cue data from the DNSs, and in particular we study how this performance depends on correlations between encounters. Among other results, we find that quasioptimal strategies continue to outperform heuristics in the presence of strong mean flow but fail to do so in the absence of a mean flow. We also explore how to choose optimal search parameters, including the frequency and threshold concentration of observation.