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This study presents an approach to two-pulse 3D particle tracking using methods developed within the Shake-The-Box (STB, Schanz et al. in Exp Fluids 57:70, 2016) Lagrangian particle tracking (LPT) framework. The original STB algorithm requires time-resolved data and reconstructs 3D trajectories using a particle position prediction–correction scheme. However, dual-frame 3D acquisition systems, consisting of a dual-cavity laser and double-frame cameras, remain commonly used for many particle-image-based investigations in a wide range of flow velocities and applications. While such systems can be used to capture short Multi-Pulse particle trajectories (Multi-Pulse STB, MP-STB—Novara et al. in Exp Fluids 57:128, 2016a; Novara et al. in Exp Fluids 60:44, 2019), the most widespread application is still a single-pulse illumination of each of the two available frames. As a consequence, 3D LPT approaches capable of dealing with two-pulse recordings are of high interest for both the scientific community and industry. Several methods based on various evaluation schemes have been developed in the past. In the present study, a Two-Pulse Shake-The-Box approach (TP-STB) is proposed, based on the advanced IPR algorithm presented by Jahn et al. (Exp Fluids 62:179, 2021), in combination with an iterative scheme of reconstruction and tracking, ideally with the help of a predictor gained by Particle Space Correlation. It basically constitutes a lean version of the MP-STB technique, with lower demands on experimental setup and processing time. The performances of TP-STB are assessed by means of comparison with the results from the time-resolved STB algorithm (TR-STB) both concerning synthetic and experimental data. The suitability of the algorithm for the analysis of dual-frame 3D particle imaging datasets is assessed based on the processing of existing images from a tomographic PIV experiment from 2012. The comparison with the results published by Henningsson et al. (J R Soc Interface 12:20150119, 2015) confirms the capability of TP-STB to accurately reconstruct individual particle tracks despite the limited time-resolution information offered by two-frame recordings.

To study airborne transport of aerosol particles by mixed convection and dynamic situations within a closed room, the Cottbus Aerosol Particle Reference Experiment (CARE) was built and equipped, which includes thermal manikins and a spreader dummy. For various flow configurations (location of spreader, heating bodies, windows opened, air ventilation with and without air purification systems) flow visualisation was performed, particulate matter sensors (PMS) measured local particle concentrations, head-mounted camera systems counted particle concentrations of individuals and finally, large field of view Shake-The-Box Particle Tracking delivered velocity fields. The comprehensive experimental configuration of different measurement systems are discussed in terms of their aerosol transport properties and quantitative results, effective application and comparative efficiency explaining the flow dynamics. The findings from these experiments also provide information under which circumstances particularly high concentrations of aerosol particles can be found on which locations.

In the past few decades various particle image–based volumetric flow measurement techniques have been developed that have demonstrated their potential in accessing unsteady flow properties quantitatively in various experimental applications in fluid mechanics. In this review, we focus on physical properties and circumstances of 3D particle–based measurements and what knowledge can be used for advancing reconstruction accuracy and spatial and temporal resolution, as well as completeness. The natural candidate for our focus is 3D Lagrangian particle tracking (LPT), which allows for position, velocity, and acceleration to be determined alongside a large number of individual particle tracks in the investigated volume. The advent of the dense 3D LPT technique Shake-The-Box in the past decade has opened further possibilities for characterizing unsteady flows by delivering input data for powerful data assimilation techniques that use Navier–Stokes constraints. As a result, high-resolution Lagrangian and Eulerian data can be obtained, including long particle trajectories embedded in time-resolved 3D velocity and pressure fields.