Lattice-Boltzmann simulation of heat transfer in turbulent pipe flows seeded with resolved non-spherical particles

• Applicant: Professor Dr. Dominique Thévenin
• Subject Area: Fluid Mechanics
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 465872891

Particle-laden flows are prevalent in a wide range of natural and industrial processes, including energy conversion, chemical reactors, and environmental systems. In many of these applications, the suspended particles are of finite size, thermally active, and exhibit non-spherical geometries. Despite their practical relevance, the fundamental understanding of coupled momentum and heat transport mechanisms in such turbulent multiphase flows remains limited. Existing numerical and experimental studies often rely on simplifying assumptions, such as spherical particles treated as points in isothermal conditions, which constrain their applicability to real-world systems. This research project seeks to advance the fundamental understanding of thermal particulate turbulence by performing direct numerical simulations (DNS) of turbulent pipe flows laden with non-spherical, thermally active particles whose characteristic dimensions exceed the local Kolmogorov scale. The computational framework combines the lattice Boltzmann method (LBM) as the fluid solver with an immersed boundary method (IBM), and is extended to capture heat transfer through the integration of a finite-difference scheme. The study address several open questions: How do particle shape, orientation dynamics, and spatial clustering influence convective heat transport and turbulence modulation? What thermal effects emerge from particle–fluid and inter-particle interactions, and how do these interactions alter drag, thermal behavior, and heat flux distributions? How does heat transfer affect particle statistics, including spatial distribution, translational and rotational velocities, and preferred orientation? What is the role of internal anisotropic heat conduction, large Biot numbers, and interfacial heat transfer in pipe flow transport processes? To bridge scales and support practical application, the project further aims to develop machine learning (ML)-based surrogate models trained on DNS data. These reduced-order models will enable efficient predictions suitable for system-level simulations.

• DFG Programme: Research Grants