Single and multi-objective optimization of nanofluid flow in flat tube to enhance heat transfer using antlion optimizer algorithms

This optimization problem focuses to determine the five independent design variables to find out the optimal values of heat transfer coefficient ( $$\stackrel{\sim }{H}$$ H ∼ ) and pressure drop $$(\Delta P)$$ ( Δ P ) parameters. It consists of two conflicting optimization problem as discrete objective functions: first to maximize heat transfer coefficient and second to minimize pressure drop value. In this work, the problem is optimized using two approaches: First, single objective approach for both the objective functions separately to determine the optimal values of design variables using classical ALO and its modified variants. Secondly, multi-objective approach in which both the objective functions are optimized simultaneously to determine objective function values of heat transfer coefficient and pressure drop while optimizing design variables. This purpose is achieved using two different methods of multi-objective optimization: (i) Using weighted sum approach of multi-objective optimization using classical ALO and its proposed variants (ii) Pareto based multi-objective optimization using multi-objective antlion optimizer. The model used in this work is developed using Al2O3-water nanofluid using horizontal flat tube with the help of computational fluid dynamics and response surface methodology (RSM). The obtained results show superiority of ALO and its modified variants approach and also compared with RSM.