Numerical investigation on pillow plate heat exchangers: Effects of nanofluid and geometry

Abstract

Abstract The current research involves a comprehensive numerical simulation of nanofluid flow within a pillow-plate heat exchanger. Al2O3-water nanofluid and water are used as the working fluids, simulated using a two-phase mixture model. The study explores the influence of geometric properties on the heat exchanger’s hydrodynamic and thermal performance. It also delves into the utilization of nanofluids as the working medium and its impact on dimensionless pressure drop, Nusselt numbers, and dimensionless temperature. An innovative aspect of this research lies in the integration of artificial intelligence (AI) techniques for heat exchanger design optimization. Specifically, a Random Forest Regressor (RFR) AI model is employed to predict crucial parameters, including heat transfer coefficient (HTC) and pressure drop, based on input design variables. Key findings reveal that non-linear hole layouts in heat exchangers significantly improve Nusselt numbers by up to 25 percent. Conversely, larger holes result in higher pressure drops. The use of nanofluids enhances thermal efficiency by up to 10% while increasing pressure drop by around 7%.