Chapters authored
Pressure Drop and Boiling Heat Transfer Characteristics of R410A in Macro-Scale and Mini-Scale Channels
This chapter demonstrates the two‐phase flow pressure drop and heat transfer of R410A during boiling in various tube types. The pressure drop and local heat transfer coefficients were obtained for heat fluxes ranging from 10 to 40 kW/m2, mass fluxes ranging from 100 to 600 kg/m2s, the vapour quality up to 1.0 and the saturation temperatures of 5–15°C. The test sections were made of various tube diameters of 1.5, 3.0, 6.61 and 7.49 mm, respectively. The effect of mass flux, heat flux, saturation temperature and inner tube diameter on pressure drop and heat transfer coefficient was analysed. The experimental results were compared against several existing pressure drop and heat transfer coefficient correlation. New correlations of pressure drop and boiling heat transfer coefficient were also developed in this present study.
Part of the book: Heat Exchangers
Optimization Design by Coupling Computational Fluid Dynamics and Genetic Algorithm
Nowadays, optimal design of equipment is one of the most practical issues in modem industry. Due to the requirements of deploying time, reliability, and design cost, better approaches than the conventional ones like experimental procedures are required. Moreover, the rapid development of computing power in recent decades opens a chance for researchers to employ calculation tools in complex configurations. In this chapter, we demonstrate a kind of modern optimization method by coupling computational fluid dynamics (CFD) and genetic algorithms (GAs). The brief introduction of GAs and CFD package OpenFOAM will be performed. The advantage of this approach as well as the difficulty that we must tackle will be analyzed. In addition, this chapter performs a study case in which an automated procedure to optimize the flow distribution in a manifold is established. The design point is accomplished by balancing the liquid-phase flow rate at each outlet, and the controlled parameter is a dimension of baffle between each channel. Using this methodology, we finally find a set of results improving the distribution of flow.
Part of the book: Computational Fluid Dynamics