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

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-known insect behavior "casting") followed by a return to the starting location. The zigzag motion generates characteristic <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msup><mml:mi>t</mml:mi> <mml:mrow><mml:mn>1</mml:mn> <mml:mo>/</mml:mo> <mml:mn>2</mml:mn></mml:mrow> </mml:msup> </mml:mrow> </mml:math> scaling of the rms displacement envelope. By passing to the limit where the probability of detection vanishes, we connect these results to the classical linear search problem and derive an estimate of the tail of the arrival time pdf as a stretched exponential <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>p</mml:mi> <mml:mo>(</mml:mo> <mml:mi>T</mml:mi> <mml:mo>)</mml:mo> <mml:mo>∼</mml:mo> <mml:mtext>exp</mml:mtext> <mml:mo>(</mml:mo> <mml:mo>-</mml:mo> <mml:mi>k</mml:mi> <mml:msqrt><mml:mi>T</mml:mi></mml:msqrt> <mml:mo>)</mml:mo></mml:mrow> </mml:math> for some <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>k</mml:mi> <mml:mo>></mml:mo> <mml:mn>0</mml:mn></mml:mrow> </mml:math> , in agreement with Monte Carlo results. We also discuss what happens as the wind speed becomes smaller.

Authors

Institutions

Publication Details

Journal
Physical Review Fluids
Published
2025-04-09
DOI
https://doi.org/10.1103/physrevfluids.10.044601
Citations
3
Primary Topic
Insect Pheromone Research and Control
Type
article
Field-Weighted Citation Impact
5.02

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Massimo Vergassola, Luca Biferale, Antonio Celani, R. A. Heinonen
3 citations
Physical Review Fluids
Insect Pheromone Research and Control
5.02
article

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

Massimo Vergassola, Luca Biferale, Antonio Celani, R. A. Heinonen
article en
3 citations

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-known insect behavior "casting") followed by a return to the starting location. The zigzag motion generates characteristic <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msup><mml:mi>t</mml:mi> <mml:mrow><mml:mn>1</mml:mn> <mml:mo>/</mml:mo> <mml:mn>2</mml:mn></mml:mrow> </mml:msup> </mml:mrow> </mml:math> scaling of the rms displacement envelope. By passing to the limit where the probability of detection vanishes, we connect these results to the classical linear search problem and derive an estimate of the tail of the arrival time pdf as a stretched exponential <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>p</mml:mi> <mml:mo>(</mml:mo> <mml:mi>T</mml:mi> <mml:mo>)</mml:mo> <mml:mo>∼</mml:mo> <mml:mtext>exp</mml:mtext> <mml:mo>(</mml:mo> <mml:mo>-</mml:mo> <mml:mi>k</mml:mi> <mml:msqrt><mml:mi>T</mml:mi></mml:msqrt> <mml:mo>)</mml:mo></mml:mrow> </mml:math> for some <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>k</mml:mi> <mml:mo>></mml:mo> <mml:mn>0</mml:mn></mml:mrow> </mml:math> , in agreement with Monte Carlo results. We also discuss what happens as the wind speed becomes smaller.

Physical Review FluidsVol. 10(4)
University of Rome Tor Vergata (IT), The Abdus Salam International Centre for Theoretical Physics (ICTP) (IT), Université Paris Sciences et Lettres (FR), École Normale Supérieure (BI)
Horizon 2020 Framework Programme
Climate action
Openalex Percentile: Top 6%
Insect Pheromone Research and Control
5.02
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.