Universiti Teknologi Malaysia

About: Universiti Teknologi Malaysia is a education organization based out in Johor Bahru, Malaysia. It is known for research contribution in the topics: Membrane & Control theory. The organization has 21644 authors who have published 39500 publications receiving 520635 citations.

A review on preparation methods, stability and applications of hybrid nanofluids

Abstract: Hybrid nanofluid is a new class of nanofluids engineered by dispersing two different nanoparticles into conventional heat transfer fluid. Hybrid nanofluids are potential fluids that offer better heat transfer performance and thermo-physical properties than convectional heat transfer fluids (oil, water and ethylene glycol) and nanofluids with single nanoparticles. Scientific findings have indicated that hybrid nanofluid can replace single nanofluid since it provides more heat transfer enhancement especially in the areas of automobile, electro-mechanical, manufacturing process, HVAC and solar energy. In this paper, we summarized the recent progress related to preparation methods of hybrid nanofluids, factors affecting their stability, methods of enhancing thermal properties and current applications of hybrid nanofluids. Finally, some challenging issues that need to be solved for future research are discussed.

Prediction of seismic slope stability through combination of particle swarm optimization and neural network

Abstract: One of the main concerns in geotechnical engineering is slope stability prediction during the earthquake. In this study, two intelligent systems namely artificial neural network (ANN) and particle swarm optimization (PSO)---ANN models were developed to predict factor of safety (FOS) of homogeneous slopes. Geostudio program based on limit equilibrium method was utilized to obtain 699 FOS values with different conditions. The most influential factors on FOS such as slope height, gradient, cohesion, friction angle and peak ground acceleration were considered as model inputs in the present study. A series of sensitivity analyses were performed in modeling procedures of both intelligent systems. All 699 datasets were randomly selected to 5 different datasets based on training and testing. Considering some model performance indices, i.e., root mean square error, coefficient of determination (R2) and value account for (VAF) and using simple ranking method, the best ANN and PSO---ANN models were selected. It was found that the PSO---ANN technique can predict FOS with higher performance capacities compared to ANN. R2 values of testing datasets equal to 0.915 and 0.986 for ANN and PSO---ANN techniques, respectively, suggest the superiority of the PSO---ANN technique.