Characterization of modified tapioca starch solutions and their sprays for high temperature coating applications.

The objective of the research was to understand and improve the unusual physical and atomization properties of the complexes/adhesives derived from the tapioca starch by addition of borate and urea. The characterization of physical properties of the synthesized adhesives was carried out by determining the effect of temperature, shear rate, and mass concentration of thickener/stabilizer on the complex viscosity, density, and surface tension. In later stage, phenomenological analyses of spray jet breakup of heated complexes were performed in still air. Using a high speed digital camera, the jet breakup dynamics were visualized as a function of the system input parameters. The further analysis of the grabbed images confirmed the strong influence of the input processing parameters on full cone spray patternation. It was also predicted that the heated starch adhesive solutions generate a dispersed spray pattern by utilizing the partial evaporation of the spraying medium. Below 40°C of heating temperature, the radial spray cone width and angle did not vary significantly with increasing Reynolds and Weber numbers at early injection phases leading to increased macroscopic spray propagation. The discharge coefficient, mean flow rate, and mean flow velocity were significantly influenced by the load pressure but less affected by the temperature.

An algorithmic framework for multiobjective optimization.

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization.