In earlier studies, much research has focused on increasing the efficiency of heat exchanger fields. Therefore, in this study, graphene nanofluid was fabricated for use as a heat transfer medium for a heat exchanger. Graphene has excellent electrical conductivity, mechanical properties, and heat transfer properties. It is expected that the heat transfer efficiency will be improved by fabricating the nanofluid. However, graphene is prone to sedimentation, because of its cohesion due to van der Waals binding force. In this experiment, a nanofluid was fabricated with enhanced dispersibility by surfactant and the ball-milling process. The zeta potential, absorbance, and thermal conductivity of the nanofluid were measured. As a result, when using the ratio of 2:1 (graphene:sodium dodecyl sulfate (SDS)), a higher thermal conductivity was obtained than in other conditions.

We present a nanocomposite strain sensor (NCSS) to develop a novel structural health monitoring (SHM) sensor that can be easily installed in a composite structure. An NCSS made of a multi-walled carbon nanotubes (MWCNT)/epoxy composite was installed on a target structure with facile processing. We attempted to evaluate the NCSS sensing characteristics and benchmark compared to those of a conventional foil strain gauge. The response of the NCSS was fairly good and the result was nearly identical to the strain gauge. A neuron, which is a biomimetic long continuous NCSS, was also developed, and its vibration response was investigated for structural damage detection of a composite cantilever. The vibration response for damage detection was measured by tracking the first natural frequency, which demonstrated good result that matched the finite element (FE) analysis.

Water-based drilling mud (WBM) is a non-Newtonian fluid that has a variety of applications such as in transporting cuttings during drilling, protecting the borehole, and cooling the drill bit. With the development of nano-technology, various nanoparticles have been synthesized and have been added to WBM to improve its performance. Shear thinning is the most important factor in drilling mud and this attribute can be improved when two-dimensional particles are added. MoS₂ nanoparticles, which represent a typical two-dimensional material, are easy to synthesize in large quantities and have a high thermal conductivity and low coefficient of friction. Since the two-dimensional structure, thermal conductivity, and low coefficient of friction of MoS₂ would improve the performance of WBM, we experimented with MoS₂ nanosheets as an additive, under optimal conditions, using various samples each with uniform sizes and thicknesses of nanosheets. A large amount of MoS₂ nanosheets was synthesized, sorted by thickness and diameter, and added to drilling mud. The diameter of MoS₂ was divided into a small diameter group (about 100–400 nm) and a big diameter group (about 300–650 nm), and the thickness was divided into 1–2 nm and 5–10 nm groups. Experimental results showed that when MoS₂ is added to WBM, shear thinning occurs more strongly. In addition, the addition of MoS₂ with a thickness of 1–2 nm and a diameter of 300–650 nm resulted in the highest increase in viscosity and thermal conductivity of WBM. As a result, we experimentally confirmed that MoS₂ can be used as an additive to increase the thermal conductivity and viscosity of WBM and to make shear thinning phenomenon more.

Recently, global energy consumption has increased due to industrial development, resulting in increasing demand for various energy sources. Aside from the increased demand for renewable energy resources, the demand for fossil fuels is also on the rise. Accordingly, the demand for resource development in the deep sea is also increasing. Various systems are required to efficiently develop resources in the deep sea. A study on an in-line type oil–water separator is needed to compensate for the disadvantages of a gravity separator that separates traditional water and oil. In this paper, the separation performance of the axial-flow oil–water separator for five design variables (conical diameter, conical length, number of vanes, angle of vane, and thickness of vane) was analyzed. Numerical calculations for multiphase fluid were performed using the mixture model, one of the Euler–Euler approaches. Additionally, the Reynolds stress model was used to describe the swirling flow. As a result, it was found that the effect on the separation performance was large in the order of angle of vane, conical diameter, number of vanes, the thickness of vane, and conical length. A neural network model for predicting separation performance was developed using numerical calculation results. To predict the oil–water separation performance, five design parameters were considered, and the evaluation of the separation performance prediction model was compared with the multilinear regression (MLR) model. As a result, it was found that the R square was improved by about 74.0% in the neural network model, compared with the MLR model.