Experimental investigation of turbulence and turbulent thermal diffusion in strongly inhomogeneous and anisotropic forced convection

We investigate properties of turbulence and turbulent transport of non-inertial particles described in terms of turbulent thermal diffusion in strongly inhomogeneous and anisotropic convection forced by two similar turbulence generators with oscillating membrane and a steady grid in the air flow (with the Rayleigh number about $10^8$). Velocity field and spatial distribution of particles are measured using Particle Image Velocimetry system. The temperature distribution is measured in many locations using a temperature probe equipped with 12 E - thermocouples. In the forced convection, the gradients of the mean temperature field and the particle number density in the horizontal direction in the core flow are much stronger than in the vertical direction. The mean fluid velocity structure show transition between a single-roll pattern for isothermal turbulence to double-roll patterns with increase of the temperature difference between the bottom and upper walls of the chamber. For larger temperature differences, the mean fluid velocity structure returns to a single-roll pattern. In the turbulent regions with large mean temperature gradients, the dominant effect of the large-scale particle clustering is turbulent thermal diffusion, resulting in that the maximum of the mean particle number density is located in the regions with minimum of the mean temperature and vise versa. Deviations from this feature is observed in the regions with strong mean fluid velocities where the mean temperature gradients are small.

💡 Research Summary

This paper presents an experimental investigation into the fundamental properties of turbulence and the transport mechanisms of non-inertial particles within a highly inhomogeneous and anisotropic forced convection environment. The study focuses on the interplay between temperature gradients and particle distribution, specifically examining the phenomenon of turbulent thermal diffusion in a system characterized by a high Rayleigh number (approximately $10^8$).

The experimental setup utilized two distinct turbulence generators—an oscillating membrane and a steady grid—to induce complex, non-uniform airflow. To capture the intricate dynamics of the system, the researchers employed Particle Image Velocimetry (PIV) to map the velocity fields and particle spatial distributions, alongside a multi-point temperature probe equipped with 12 E-type thermocouples to monitor the thermal field. A key characteristic of the studied flow is its extreme anisotropy, where the gradients of the mean temperature field and particle number density in the horizontal direction are significantly more intense than those in the vertical direction.

The study reveals a non-monotonic transition in the mean fluid velocity structure. As the temperature difference between the bottom and top walls of the chamber increases, the flow transitions from a single-roll pattern to a double-roll pattern, and subsequently reverts to a single-roll pattern at even higher temperature differences. This complex structural evolution highlights the non-linear impact of thermal buoyancy on turbulent flow topology.

The most significant scientific contribution of this work is the experimental validation of “turbulent thermal diffusion” as a driver for large-scale particle clustering. In regions characterized by large mean temperature gradients, the researchers observed that the maximum particle number density coincides with the minimum mean temperature, and vice versa. This indicates that the temperature-induced gradient effectively drives particles toward cooler regions. However, the study also identifies a critical boundary to this effect: in regions where the mean fluid velocity is high and temperature gradients are relatively small, the particle distribution deviates from this thermal diffusion-driven pattern. This suggests that the competition between turbulent-induced mechanical mixing and thermal diffusion-induced clustering determines the final spatial distribution of particles. These findings provide crucial insights into particle-laden turbulent flows, which are essential for applications in atmospheric science, industrial combustion, and cooling technologies.