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This article studies the stability of hybrid TiO2 based nanofluid by varying the ultra-sonication time, weight fraction between carbon and TiO2 and type of surfactant. The objective of this research are synthesise volume fraction of 0.1% C - TiO2 hybrid nanofluid with 15ml of distilled water and study the effect of weight fraction between C and TiO2, type of surfactant and ultra-sonication time to the stability of hybrid nanofluid. Weight fraction between C – TiO2 will be varied at 0% - 100%, 10% - 90%, 50% - 50%, 90% - 10% and 100% - 0%. Different ultrasonication time will be used which are 1, 3 and 5 hours to determine the optimum ultrasonication time for the hybrid nanofluid. Sodium dodecyl sulphate (SDS) and Triton X-100 will be added into hybrid nanofluid to determine the effect of surfactant to the stability of hybrid nanofluid. The morphology and particle size characteristic of TiO2 and carbon powder is evaluated by using X-ray diffraction and FESEM. It is shown that TiO2 has particle size of around 30nm while carbon powder used is flat shaped. In this research, two steps method is used to synthesise the hybrid nanofluid. After mixing the two powder together, it will be mixed with distilled water by magnetic stirring for 3 hours. Then ultrasonication is included in synthesise of hybrid nanofluid to prevent agglomeration of particles. The pH values of each hybrid nanofluid is measured by using pH meter. Two stability evaluations are carried out which are centrifugation and zeta potential measurement. Centrifugation is carried out right after hybrid nanofluid is done ultra-sonicated for 30 minutes at 2000 RPM. Hybrid nanofluid that is ultra-sonicated for 3 hours resulted to achieve highest stability with highest value of zeta potential of -21.8 mV. 100% C - 0% TiO2 hybrid nanofluid has the highest zeta potential measurement of -37.6 mV. Finally, for type of surfactant, SDS able to enhance stability of hybrid nanofluid more than Triton X-100. The zeta potential of hybrid nanofluid after addition of SDS reached -47.0 mV which is higher than Triton X-100 which only achieve zeta potential measurement of -24.3 mV.

The problem of 3-Dimensional en-route airspace sectorization has been modelled as a multi-objective optimization by taking into account of conflicting air traffic controller workloads. In practice, air traffic controllers impose limits on these objectives, which may not be captured completely in Pareto front obtained using the multi-objective model. Hence, in this paper, we propose a preference-based multi-objective optimization model for 3-dimensional en-route sectorization. Through the use of reference point(s), the proposed model is able to find multiple solutions that satisfy the air traffic controller preference(s) faster. Population-based NSGA-II has been used to solve the preference-based 3-dimensional en-route sectorization problem. The performance of preference based sectorization is evaluated using actual flight data from the Singapore regional airspace. The results are compared with conventional multi-objective optimization model which integrates the preference as a constraint. Results indicate that the preference-based model generally performs better than the constraint-based model. Further, multiple parallel runs of preference-based optimization could provide a greater variety of choices in airspace sectorization for the air traffic controllers.

This paper presents an automatic airspace sectorization method for air traffic management in presence of many climbing trajectories. For this purpose, we have modified the formulation, and solved it using Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Voronoi diagrams. The air traffic controller's monitoring workload is formulated with the use of modified dynamic density, which is a more rigorous and accurate formulation as compared to the simplistic metrics used in other works. The multi-objective formulation is implemented using historical flight data within Singapore regional airspace. The results clearly indicate that the en-route 3D sectorization using dynamic density in monitoring workload formulation provides better workload balance in ATC compared to 2D sectorization.