Journal of Energy Engineering

Improving the quality of fuel represents an effective solution to address the long-standing issue of engine emissions. This study adopts ${\mathrm{CeO}}_2$ and carbon nanotubes as additives, which are blended with neat diesel using a physicochemical method to produce nanofuel. The research investigated the combustion and emission performance of a high-pressure common rail four-cylinder engine under various operating conditions, utilizing different types of nanomaterials, nanoparticle sizes, and biofuel substitutes as variables. The outcomes of the study indicate superior performance of the nanofuel compared with neat diesel across all evaluated aspects. Notably, the size of the added particles directly influenced the beneficial enhancements achieved in diesel fuel. Additionally, the nanofuel containing carbon nanotubes demonstrated a greater improvement effect than the one incorporating ${\mathrm{CeO}}_2$. Furthermore, when compared with neat diesel, the combustion of carbon nanotubes (CNT) nanofuel at 100% load resulted in a notable 4.0% increase in brake thermal efficiency, coupled with reductions of 3.0% in brake specific fuel consumption and 8.8%, 4.4%, 4.9%, and 9.6% reductions, respectively, for carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (${\mathrm{NO}}_{\mathrm{x}}$), and smoke emissions. It is noteworthy that the incorporation of biofuel alternatives, such as biodiesel and ethanol, into nanofuels exhibited even further enhanced effects in terms of promoting combustion and reducing emissions. The findings of this study serve as an important reference for selecting suitable nanofuels tailored to meet diverse operational demands of engines.

In subsurface energy extraction, permeability or conductivity is the vital parameter for quantifying fluid flow in porous media. Three-dimensional digital core technique is widely used to calculate flow parameters and to analyze the internal structure and properties of rocks. However, one major problem is its high computational cost associated with fine-scale simulation of porous media, especially for large and complex rock samples. In this study, we propose to use subvolume properties to increase computational efficiency. Specifically, we first construct digital cores of dune sand and sandstone by CT scanning technology, and divide the whole core into multiple subvolumes and calculate their permeabilities. Then, we reassemble the subvolumes and compute the permeability for the whole core. This approach may lead to underestimation as the connectivity between subvolumes could be lost. To address this issue, we divide the whole core into different-sized subvolumes, and then use curve fitting to deduce the permeability of whole rock sample via extrapolation. The results show that the proposed method has improved accuracy, and is significantly faster than simulating the whole digital core, since the computation on subvolumes can be easily parallelized. This approach provides new ideas for accurate and efficient permeability estimation for digital porous media.