The improvement of dielectric and thermal properties of insulating oils used in High Voltage (HV) equipment has been a key part of research over the last three decades. In this study, a natural ester oil is used as matrix oil and dispersions of two semi-conducting nanoparticles (NPs), titanium oxide (TiO₂) and silicon carbide (SiC), are prepared in 0.004 weight percentage ratio (0.004% w/w). In terms of thermal features, thermal diffusivity and thermal conductivity are measured over a large temperature range between 25 and 90 °C. By means of the dielectric properties, the Lightning Impulse Breakdown Voltage (LI BDV) of nanofluids (NFs) is measured and compared to the corresponding ones of the matrix. Moreover, the dielectric constant and dielectric dissipation factor (DDF) are determined for the NF and matrix samples in the frequency range of 1 to 10⁶ Hz. The obtained results show that the NF containing SiC NPs is characterized from the greatest increase in thermal conductivity, by 58% with respect to natural ester oil’s, as well as the lowest dielectric constant in both 25 and 90 °C, most likely due to their higher permittivity and thermal conductivity. Lastly, the LI BDV of the same NF is the most enhanced, by 10% relatively to the matrix, while a numerical model in COMSOL Multiphysics is developed and is found to successfully verify the LI BDV results.

This study is focused on exploring the change in the dielectric and cooling efficiency of vegetable oil following the integration of semiconducting SiC and TiO ₂ nanoparticles in two different weight percentages by comparing the corresponding behavior of nanofluids at the time of their synthesis and after one month of ageing. The thermal response has been evaluated by means of diffusivity and conductivity, while dielectric properties are measured with non-destructive tests (relative permittivity and dissipation factor) and destructive ones in terms of lightning impulse withstand voltage. The results of the latter property measurements are also compared to numerical model findings. The analysis of results proves that the properties under study are worsened with increase in nanoparticles’ concentration, probably attributing it to quicker agglomeration, as it is also confirmed by the simulation results.

We study experimentally the flow of non-wetting ganglia during the co-injection of n-heptane and water in a predominantly 2D PMMA micromodel, which is constructed based on a stochastic digital algorithm. The dynamics of the phase distribution patterns are recorded optically and post-processed using cluster identification and motion tracking algorithms in order to study the characteristics and the interactions between the mobile and stranded ganglia populations. We focus primarily on the effects of the capillary number (Ca) and the ratio of the injection flow rates (Q) on the observed ganglia size distributions and the effective two-phase rheology. Our experimental setup allows for the study of ganglia fragmentation and coalescence dynamics over five orders of magnitude (in terms ganglia sizes), thus offering novel physical insight on the pore-scale characteristics of different ganglia populations and on how their interactions determine the relative permeability of the non-wetting phase. We demonstrate that the rates of ganglia fragmentation and coalescence intensify at higher Ca values, as viscous forces become dominant over capillary ones, leading to a log-normal size distribution that shifts toward smaller mean values. This effect is directly correlated with the emergence of new flow paths that develop progressively through narrower pores-throats, where the continuous wetting phase sweeps ganglia with sizes smaller than the mean pore-throat diameter. These flow paths further contribute to the Darcy scale velocity of the non-wetting phase, thus leading to a power-law Darcian regime at intermediate Ca values with a scaling exponent that is found to be a function of Q.

We study experimentally the dynamics of non-wetting blobs flowing simultaneously with a wetting fluid in a quasi-two-dimensional porous medium consisting of random obstacles. The blobs continuously merge, forming larger ones (coalescence), and break up into smaller ones (fragmentation), leading to an overall dynamic equilibrium between the two processes. We develop a clustering algorithm for the identification of fragmentation and coalescence events that records the size of the blobs prior to and immediately after each event from high-resolution videos of the immiscible flow experiments. The results provide significant insight into the main physical features of these two processes, such as blob size distributions, breakup and coalescence frequency as a function of total flow rate, and the size distributions of the blobs formed by either the fragmentation or coalescence of other ones. One of the salient features of the fragmentation process in our study is that blobs that are smaller than the typical pore size exhibit a higher probability of producing two almost identical children (in size), whereas larger blobs break up into two children of different sizes. In the latter case, one of the children is found to have a dimension that is practically equal to the typical pore size. Our experimental results are also interpreted in the framework of a mean-field approach, where the dynamics of the blob sizes is expressed through an integro-differential population balance equation that comprises terms for the description of the rates of size gains and losses by either fragmentation or coalescence. We recover appropriate expressions for the relevant coalescence and fragmentation kernels, as functions of the blob sizes that participate in each event. A rather surprising result is that for a given blob size population, we obtain an equilibrium between the gains by fragmentation and the losses by coalescence. Furthermore, the opposite is also true, as the population gains by coalescence are found to be equal to the losses by fragmentation.

Clean energy systems are at the forefront of current discussions on the protection of our environment against global warming