Heat transfer in drop-laden low-Prandtl-number channel turbulence

In this work, we numerically investigate heat transfer in low-Prandtl-number drop-laden wall-bounded turbulence. These flows are characteristic of nuclear and fusion technologies, where liquid metals – known for their high thermal conductivity – are laden with drops or bubbles of another liquid or pressurised gas. To this end, we consider forced convection turbulence between two differentially heated parallel plates. The carrier phase (i.e. liquid metal) is characterised by a low Prandtl number $Pr_c=0.013$ , while for the dispersed phase, we explore a range of Prandtl numbers from $Pr_d=0.013$ (matched case) to $Pr_d=7$ (super-unitary Prandtl number in the dispersed phase). Simulations are conducted at constant friction Reynolds number $Re_\tau =300$ , and for each dispersed phase Prandtl number, two volume fractions are examined: $\alpha =5.4\,\%$ and $\alpha =10.6\,\%$ . The simulation framework relies on direct numerical simulation of the Navier–Stokes equations, coupled with a phase-field method and the energy equation. Results show that an increase of the dispersed phase Prandtl number reduces heat transfer, leading to a lower Nusselt number for both volume fractions. To explain this behaviour, we analyse how the drops modify the temperature field, and demonstrate that the heat transfer reduction stems from a decreased diffusive heat flux within the dispersed phase. Finally, we propose a phenomenological model to predict the Nusselt number as a function of both the dispersed phase volume fraction and Prandtl number.

Authors

Institutions

Publication Details

Journal
Journal of Fluid Mechanics
Published
2025-07-16
DOI
https://doi.org/10.1017/jfm.2025.10372
Citations
2
Primary Topic
Particle Dynamics in Fluid Flows
Type
article
Field-Weighted Citation Impact
2.29

Funders

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

Heat transfer in drop-laden low-Prandtl-number channel turbulence

Davide Procacci, Alfredo Soldati, Alessio Roccon, Jannike Solsvik
2 citations
Journal of Fluid Mechanics
Particle Dynamics in Fluid Flows
2.29
article

Heat transfer in drop-laden low-Prandtl-number channel turbulence

Davide Procacci, Alfredo Soldati, Alessio Roccon, Jannike Solsvik
article en
2 citations

Abstract

In this work, we numerically investigate heat transfer in low-Prandtl-number drop-laden wall-bounded turbulence. These flows are characteristic of nuclear and fusion technologies, where liquid metals – known for their high thermal conductivity – are laden with drops or bubbles of another liquid or pressurised gas. To this end, we consider forced convection turbulence between two differentially heated parallel plates. The carrier phase (i.e. liquid metal) is characterised by a low Prandtl number $Pr_c=0.013$ , while for the dispersed phase, we explore a range of Prandtl numbers from $Pr_d=0.013$ (matched case) to $Pr_d=7$ (super-unitary Prandtl number in the dispersed phase). Simulations are conducted at constant friction Reynolds number $Re_\tau =300$ , and for each dispersed phase Prandtl number, two volume fractions are examined: $\alpha =5.4\,\%$ and $\alpha =10.6\,\%$ . The simulation framework relies on direct numerical simulation of the Navier–Stokes equations, coupled with a phase-field method and the energy equation. Results show that an increase of the dispersed phase Prandtl number reduces heat transfer, leading to a lower Nusselt number for both volume fractions. To explain this behaviour, we analyse how the drops modify the temperature field, and demonstrate that the heat transfer reduction stems from a decreased diffusive heat flux within the dispersed phase. Finally, we propose a phenomenological model to predict the Nusselt number as a function of both the dispersed phase volume fraction and Prandtl number.

Journal of Fluid MechanicsVol. 1015
University of Udine (IT), TU Wien (AT), Norwegian University of Science and Technology (NO)
Technische Universität Wien
Openalex Percentile: Top 12%
Particle Dynamics in Fluid Flows
2.29
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.