TURB-Smoke. A database of Lagrangian pollutants emitted from point-sources and dispersed in turbulent flows

TL;DR

TURB-Smoke is a comprehensive numerical dataset from high-fidelity simulations of turbulent flows with multiple pollutant sources, aiding the development of source-tracking algorithms in environmental monitoring.

Contribution

It introduces a detailed, large-scale Lagrangian particle dataset from direct numerical simulations for studying pollutant dispersion in turbulence.

Findings

Provides ground-truth data for source-tracking in turbulent flows

Includes trajectories and concentration fields for algorithm testing

Enables evaluation of sensor and mobile agent strategies

Abstract

Identifying the location and characteristics of pollution sources in turbulent flows is challenging, especially for environmental monitoring and emergency response, due to sparse, stochastic, and infrequent cue detection. Even in idealized settings, accurately modeling these phenomena remains highly complex, with realistic representations typically achievable only through experimental or simulation-based data. We introduce TURB-Smoke, a cutting-edge numerical dataset designed for investigating odor and contaminant dispersion in turbulent environments with and without mean wind. Generated via direct numerical simulations of the fully resolved three-dimensional Navier-Stokes equations, TURB-Smoke tracks hundreds of millions of Lagrangian particles released from five distinct point sources in fully developed turbulence, thus providing a reliable ground-truth framework for developing and evaluating source-tracking strategies using stationary sensors or mobile agents in realistic flows. Each particle's trajectory is continuously tracked on many characteristic turbulence timescales, recording both the position and the local flow velocity. Additionally, we provide coarse-grained concentration fields in 3D and in quasi-2D slabs containing the source, ideal for quickly testing and optimizing search algorithms under varying flow conditions.