Nanoparticle-Enhanced Phase Change Materials (NePCM) have garnered significant attention in engineering literature due to their enhanced thermo-physical properties. However, their behavior during phase change process, such as melting or solidification, remains inadequately understood. This study focuses on investigating the melting process of NePCM in a square cavity, exploring distinct cases of melting from both the top and bottom sides. Notably, this work delves into the effect of thermosolutal convection on NePCM melting for the first time in the literature. The NePCM comprises copper nanoparticles (2 nm in size) suspended in water. We examine various combinations of constant temperature boundary conditions and particle volume fractions. Employing a numerical model based on the one-fluid mixture approach combined with the single-domain enthalpy-porosity model, we capture the phase change process and particles’ interaction with the solid–liquid interface. In our investigation, the hot side temperature ranges between 290 K and 300 K, while the cold side temperature remains fixed at 270 K. The mass fraction of particles (ϕ_w) varies from 0.5% to 10%. When melting NePCM from the top side, convection effects are suppressed, resulting in a melting process primarily governed by conduction. Both NePCM and pure water melt at the same rate under these conditions. However, melting NePCM from the bottom side induces convection-dominated melting. In the case of pure water, thermal convection leads to the formation of convection cells during melting. In contrast, melting NePCM triggers thermosolutal convection due to temperature and particle concentration gradients. The flow cells formed from thermosolutal convection in NePCM differ from those in pure water driven by pure thermal convection. Our simulations reveal that thermosolutal convection contributes to decelerating the solid–liquid interface, thereby prolonging NePCM melting compared to pure water. For example, for a mass fraction of particles (ϕ_w = 10%), NePCM melts 6% slower compared to pure water. Surprisingly, the increase in viscosity of NePCM plays a minimal role in the deceleration process, contrary to prior literature attributing slowdowns of the NePCM melting process primarily to increased viscosity.

At the beginning of the second half of the twentieth century, Proudman and Pearson (J. Fluid. Mech.,2(3), 1956, pp.237–262) suggested that the functional form of the drag coefficient (C_D) of a single sphere subjected to uniform fluid flow consists of a series of logarithmic and power terms of the Reynolds number (Re). In this paper, we will explore the validity of the above statement for Reynolds numbers up to 10⁶ by using a symbolic regression machine learning method. The algorithm is trained by available experimental data and data from well-known correlations from the literature for Re ranging from 0.1 to 2×10⁵. Our results show that the functional form of C_D contains powers of log(Re), plus the Stokes term. The logarithmic C_D expressions can generalize (extrapolate) better beyond the training data than pure power series of Re and are the first in the literature to predict with acceptable accuracythe onset of the rapid decrease (drag crisis) of C_D at high Re, but also to follow the right behaviour towards zero Re. We also find a connection between the root of the Re-dependent terms in the C_D expression and the first point of laminar separation. The generalization behaviour of power-based drag coefficient equations is worse than logarithmic-based ones, especially towards the zero Re regime in which they give non-physical results. The logarithmic based C_D correctly describes the physics from the low Re regime to the onset of the drag crisis. Also, by applying a minor modification in the logarithmic based equations, we can predict the drag coefficient of an oblate spheroid in the high Re regime.

Self-propelled nanofluids (SPNFs), are suspensions that contain active particles, that self-propel by converting some form of energy to mechanical work. This theoretical investigation considers the heat transfer mechanisms that may exist in an SPNF. Equations describing the effective mass diffusivity of spherical and rod-shaped particles were taken from the literature and used in analysis of particles of different shapes, aspect ratios, swimming velocities and suspended in water and in ethylene glycol. The analysis showed that the effective mass diffusivity of the particles was up to three orders of magnitude higher than the thermal diffusivity of the solvent. The enhancement of the mass diffusivity leads to a significant enhancement of thermal conductivity of up to an order of magnitude higher compared to that of the pure solvent. It is further discussed that in SPNFs, dispersion and clustering of particles and active turbulence mechanisms may add extra enhancement to the thermal transport. Recent experimental investigations supporting our findings are discussed. Combining this enhancement in thermal transport with the reported reduction of the viscosity observed for an SPNF consisting of Artificial Bacterial Flagella (ABF) particles, will help to create a highly efficient coolant. This can help to reduce energy consumption in a wide variety of the economic sectors, especially in the computing and data storage.

Nanostructured phase change materials (NEPCM) colloidal suspensions, got the attention from the scientific community due to their promising thermal properties that allow for faster solidification, and melting times. However, most of the experimental investigation shows the opposite, that the melting and freezing times are increased as the volume of the particles is increased. This investigation will be the first in the literature to include the mass transport of the particles, for the case of melting of NEPCM that will help understanding better the melting process of the NEPCM. In this paper, the development of the solid-liquid interface, the distribution of the nanoparticle profiles, as well as the development of the thermal convection, will be investigated for the case of melting of nanostructured phase change materials (NEPCM) colloidal suspensions, inside a rectangular cavity. The numerical model is based on the one-fluid-mixture approach combined with the single-domain enthalpy-porosity model for phase change. The linear dependence of the liquids, and solidus temperatures with the concentration of the nanoparticles was assumed. The NEPCM consists of water and copper nanoparticles, the nanoparticle size was selected to be 5 nm and 2 nm. The suspension was melted inside a rectangular cavity, heated from the left side. It was observed that for the case of dp = 2 nm, as the mass fraction of the particles increases the solid-liquid interface changes from a planar to unstable morphology during melting. Furthermore, as the mass fraction of the particles increases the temperature of the suspension decreased similarly to experimental observations. Also, I found that the rate of the rejection of the particles and the particles size plays an essential role in the development of the liquid fraction, concentration field, and the resulted thermal convection.

The rheology of suspensions of high-inertia (or granular) non-spherical particles characterized by high particle Stokes and Reynolds numbers is rarely investigated. In this study, we investigate the rheology of suspensions of inertial rod-like particles of aspect ratio 4 subjected to shear flow. In particular, the effect of fluid medium (air, water) against dry granular simulations on the developed stresses is assessed. CFD-DEM simulations are performed for a periodic shear box for a range of shear rates and volume fractions of particles. The dependence of rheological properties like shear stress, normal stress difference, pressure and relative viscosity on volume fraction, shear rate, granular temperature and the particle orientation are discussed. These results provide insight into the macroscopic rheology of suspensions of rods and demonstrate that the effect of particle shape and surrounding fluid cannot be completely ignored. Air as a fluid medium shows similar scaling as compared to dry granular simulations, but the stress values are generally lower. We observe drastic change in both scaling and values for water as fluid medium. In all cases, the rods show strong alignment in the direction of shear. This study can be further extended to develop stress closures for use in Eulerian flow models.

In this paper, we present a number of key numerical methods that can be used to study elongated particles in fluid flows, with a specific emphasis on fluidised beds. Fluidised beds are frequently used for the production of biofuels, bioenergy, and other products from biomass particles, which often have an approximate elongated shape. This raises numerous issues in a numerical approach such as particle-particle contact detection and the accurate description of the various hydrodynamic forces, such as drag, lift, and torque, that elongated particles experience when moving in a fluid flow. The modelling is further complicated by a separation of length scales where industrial flow structures that can extend for many metres evolve subject to solid-solid and solid-fluid interactions at the millimetre scale. As a result, it is impossible to simulate both length scales using the same numerical approach, and a multiscale approach is necessary. First, we outline the direct numerical simulation (DNS) approach that may be employed to estimate hydrodynamic force closures for elongated particles in a fluid flow. We then describe the key aspects of a CFD-DEM approach, which can be used to simulate laboratory scale fluidisation processes, that must be addressed to study elongated particles. Finally, we briefly consider how current industrial-scale models, which concretely assume particle sphericity, could be adapted for the simulation of large collections of elongated particles subject to fluidisation.

The twenty first century is the century of data. Machine learning data and driven methods start to lead the way in many fields. In this contribution, we will show how symbolic regression machine learning methods, based on genetic programming, can be used to solve fluid flow problems. In particular, we will focus on the fluid drag experienced by ellipsoidal and spherocylinder particles of arbitrary aspect ratio. The machine learning algorithm is trained semisupervised by using a very limited amount of data for a specific single aspect ratio of 2.5 for ellipsoidal and 4 for spherocylindrical particles. The effect of the aspect ratio is informed to the algorithm through what we call previous knowledge, for example, known analytical solutions in certain limits, or through interbreeding of different flow solutions from the literature. Our results show good agreement with literature results, while they are obtained computationally faster and with less computing resources. Also, the machine learning algorithm discovered that for the case of prolate spheroids, the difference between the drag coefficients perpendicular and parallel to the flow in the high Reynolds number regime only depend on the aspect ratio of the geometry, even when the individual drag coefficients still decrease with increasing Re.