The existing mold concept of fabricating magnetorheological elastomer (MRE) tends to encounter several flux issues due to magnetic flux losses inside the chamber. Therefore, this paper presents a new approach for enhancing particle alignment through MRE fabrication as a means to provide better rheological properties. A closed-loop mold, which is essentially a fully guided magnetic field inside the chamber, was designed in order to strengthen the magnetic flux during the curing process with the help of silicone oil (SO) plasticizers. The oil serves the purpose of softening the matrix. Scanning electron microscopy (SEM) was used to observe the surface morphology of the fabricated MRE samples. The field-dependent dynamic properties of the MREs were measured several ways using a rheometer, namely, strain sweep, frequency sweep, and magnetic field sweep. The analysis implied that the effectiveness of the MRE was associated with the use of the SO, and the closed-loop mold helped enhance the absolute modulus up to 0.8 MPa. The relative magnetorheological (MR) effects exhibited high values up to 646%. The high modulus properties offered by the MRE with SO are believed to be potentially useful in industry applications, particularly as vibration absorbers, which require a high range of stiffness.
This study introduces a novel platform to predict complex modulus variables as a function of the applied magnetic field and other imperative variables using machine learning. The complex modulus prediction of magnetorheological (MR) elastomers is a challenging process, attributable to the material's highly nonlinear nature. This problem becomes apparent when considering various possible fabrication parameters. Furthermore, traditional parametric modeling methods are limited when applied to solve larger-scale cases involving large databases. Consequently, the application of non-parametric modeling such as machine learning has gained increasing attraction in recent years. Therefore, this work proposes a data-driven approach for predicting multiple input-dependent complex moduli using feedforward neural networks. Besides excitation frequency and magnetic flux density as operating conditions, the inputs consider compositions and curing conditions represented by magnetic particle weight percentage and the curing magnetic field, respectively. Extreme learning machines and artificial neural networks were used to train the models. The simulation results obtained at various curing conditions and other inputs confirm that the predicted complex modulus has high accuracy with an R2 of about 0.997, as compared to the experimental results. Furthermore, the predicted complex modulus pattern and magnetorheological effect agree with the experimental data using both the learned and unlearned data.
Non-surfactant water-in-diesel emulsion fuel (NWD) is an alternative fuel that has the potential to reduce major exhaust emissions while simultaneously improving the combustion performance of a diesel engine. NWD comprises of diesel fuel and water (about 5% in volume) without any additional surfactants. This emulsion fuel is produced through an in-line mixing system that is installed very close to the diesel engine. This study focuses mainly on the performance and emission of diesel engine fuelled with NWD made from different water sources. The engine used in this study is a direct injection diesel engine with loads varying from 1 to 4 kW. The result shows that NWD made from tap water helps the engine to reduce nitrogen oxide (NOx) by 32%. Rainwater reduced it by 29% and seawater by 19%. In addition, all NWDs show significant improvements in engine performance as compared to diesel fuel, especially in the specific fuel consumption that indicates an average reduction of 6%. It is observed that all NWDs show compelling positive effects on engine performance, which is caused by the optimum water droplet size inside NWD.