We present a discussion of the enthalpy transport at the interface between two homothermal regions in a particle-laden turbulent flow, in order to quantify the role of inertial particles and of fluid-particle thermal interaction. We use a phase-space analysis of particles in a reduced phase space, and a formulation of a moment of total enthalpy integral (MTEI) applied to data from direct numerical simulations (DNS) at a Taylor microscale Reynolds number equal to 56 in a thermally coupled two-way regime. This allows us to identify the main features of particle statistics and to measure the effects of particle inertia and turbulent convection of the heat transfer, providing a quantitative and interpretable evaluation of the process which does not require any hypothesis of self-similarity. We show how particles dominate the transport mechanisms, bridging enthalpy across eddy structures, in particular when their Stokes number approaches unity, making them accumulate across temperature fronts.

The growing availability of GPU-accelerated open-source solvers has boosted the capability of tackling complex single-phase and multiphase turbulent flows by means of direct and large-eddy simulations. GPU-accelerated solvers can leverage the heterogeneous computing architectures that are available in leading high-performance computing centers worldwide, taking advantage of the higher throughput and greater energy efficiency offered by GPUs as compared to CPUs. However, porting CPU-based numerical solvers to GPUs entails many outstanding challenges, such as parallelism exposure, inter-GPU communication, memory allocation constraints, and shared memory limitations. To overcome these challenges, GPU-friendly algorithms, performance portability strategies, and careful selection of computational paradigms and programming languages must be developed. Besides, adaptive mesh refinement and data compression may be integrated to mitigate I-O bottlenecks and enable simulations of more complex geometries on top of the existing requirements imposed by incompressible flows. When compressibility effects become significant, further considerations related to the adoption of high-performance preconditioners and multigrid solvers become crucial for tackling large, sparse linear systems and extending simulations to high-Mach flows. Finally, reduced-precision arithmetic can further enhance performance, energy efficiency, and scalability. In this work, we survey current applications of GPU-accelerated solvers in the broad area of fluid mechanics and turbulence simulations and discuss the main challenges and bottlenecks associated with code porting and optimization. We then conclude our analysis with an outlook on future perspectives for enabling efficient GPU-based exascale computing of turbulence.

International audience

This study investigates the influence of titanium dioxide ( TiO 2 ) nanoparticles suspended in water on convection cells and heat transfer dynamics. To this end, a Rayleigh-Bénard experimental study was conducted for a water- TiO 2 nanofluid ( < 0.02 % wt) in a cylindrical cavity with a diameter to height aspect ratio of 16. A shadowgraph was used to visualize the spatial and temporal evolution of convection cells in a Rayleigh regime ( < 10 4 ), well suited for observing transitions and stability in flow patterns. The observations show that both colloidal stability and nanoparticle sedimentation strongly affect the evolution of convection patterns and heat transfer. These experiments qualitatively demonstrate that TiO 2 nanoparticles can enhance heat transfer under convective conditions, as inferred from the observed changes in convection cell pattern and flow organization. While nanofluids are typically designed to maximize colloidal stability, minimize agglomeration, and prevent sedimentation, our results challenge this paradigm by showing that sedimentation plays a key role in intensifying heat transport.