Energy Efficiency Optimization Design of a Forward-Swept Axial Flow Fan for Heat Pump

Nowadays, an air-conditioning refrigeration system and a heat pump system are widely used as temperature-regulating equipment by people all over the world. Heat pumps are heavily used in a cold climate (Xu et al., 2020). Due to the extensive use of related equipment, they have led to huge energy consumption and considerable greenhouse gas emissions while improving people’s quality of life (Xu et al., 2021). As climate warming and energy problems become increasingly prominent, relevant institutions attach more and more importance to energy and have issued corresponding standards (CONTROLS, REFRIGERATION).

The axial flow fan is the main component of an air-conditioning system and a heat pump system. Improving its working efficiency can effectively reduce the energy consumption of the temperature regulation system (Usman et al., 2017). At the same time, its aerodynamic performance has a great impact on the overall performance of the air-conditioning system and heat pump system and determines the user’s comfort level (Lim et al., 2020). According to vortex-sound theory, turbulent vortexes are the main influence factor of fan noise, so the turbulence vortexes generated by unreasonable flow inside the fan will greatly affect the efficiency of the fan. Therefore, sound is a form of energy dissipation, and optimizing fan noise can indirectly improve the efficiency of the fan (Schram and Hirschberg, 2003). At the same time, excessive noise will affect user experience. Therefore, in order to make more efficient use of energy, designers put forward higher requirements for the aerodynamic performance of axial flow fans, and the goal of making high efficiency, low noise, and large flow rate is to be pursued (Jung and Joo, 2019). Because the blade is the main power element of the axial fan, it is of great significance to explore the feasibility of its parameter optimization for the improvement of energy efficiency of a heat pump.

Various scholars have conducted extensive research on improving axial flow fan performance. Xie et al. (2019) have explored the influence of various forward-swept angles on performance parameters of axial flow fans based on a parameterized model of various forward-swept angles in cooling fans, and the results show that the average air pressure at the inlet increases continuously and the air velocity increases with the increase in the forward sweep angle of the fan. Using the non-uniform rational B-spline (NURBS) parameterization method, Liu et al. (2019) have modified axial flow fan blade profiles, and under the off-design condition, the aerodynamic performance of the axial fan is greatly improved. Through experiments and numerical simulations, Hurault et al. (2010) have assessed the effects of forward-swept designs of axial flow fans on the flow field turbulent kinetic energy, and the sweep has a significant influence on the turbulent kinetic energy downstream of the fan. Ding et al. (2019), Lin et al. (2014), Angelini et al. (2017), Lin et al. (2014), Angelini et al. (2017), and Ding et al. (2019) have further implemented a parametric optimization design based on the Bezier curve for various forms of fluid machinery. Meanwhile, Ye (2017) has optimized an underwater glider wing type based on an agent model technology.

On the contrary, there are many scholars at home and abroad to optimize fluid machinery by combining the agent model and the optimization algorithm after parameterization of the optimization target. Della Vecchia et al. (2014) used Parsec for parameterized characterization of an airfoil, combined with genetic algorithms (GAs) for optimization design, and thus obtained a winged type with good aerodynamic performance. Zhou et al. (2021) used Hicks-Henne bump functions to conduct parameterized characterization of the blades of a multi-blade centrifugal fan and combined the Kriging approximate model and NSGA-II to conduct multi-objective optimization design of the blades. After optimization, the aerodynamic performance of the blades has been significantly improved. Yang and Xiao (2014) parameterized the impeller of the pump and combined the RSM model and MGA to carry out multi-objective optimization design of the impeller, and the hydraulic performance of the pump after optimization has been significantly improved.

The aforementioned studies have included hubs of low-speed axial fans that are relatively small and have few blades. The chord length, thickness, and attack angle of various blade heights vary extensively, such that the boundary layer on the wall surface is relatively thin, leading to separation flow along the blade leading edge (Zhou et al., 2014). With the increase in load, the power capacity of the blade tip can be improved, and then the fan efficiency can be improved (T, 2017). In severe cases, channel obstruction and rotation stall are some of the consequences (Zhou, 2015). Accordingly, a parametric optimization design method can enhance an axial flow fan’s blade performance, but there are currently few reports regarding fan optimization using the Bezier curve, which can be combined with a parametric optimization design and applied to the blade tip of a forward-swept axial fan. Compared to typical axial flow fans, a blade sweep design has a certain influence in mitigating flow loss. From the static pressure distribution on the blade surface, the forward-swept moving blade increases the load distribution of the upper blade height and enhances the power capacity of the blade. The forward-swept design can reduce the turbulent kinetic energy at the blade tip and reduce the clearance flow loss (Zhang et al., 2019). The blade tip areas of forward-swept axial flow fans are much larger than their blade roots and serve as the main working areas as well as noise sources (Yang and Chen, 2002). Utilizing cubic-Bezier applications to parameterize the forward-swept curve on the blade top and selecting the flow rate and total pressure efficiency as optimization goals, this study examined the relationship between the tip sweep curve and optimization goals. The radial basis function (RBF) and Kriging models were integrated to establish an ensemble of surrogates to improve the optimization efficiency and reduce the sample size, non-dominated sorting genetic algorithm II (NSGA-II) was selected for optimizing the blade tip forward sweep curve, and numerical simulations were employed to explore shifts in the flow field and acoustic attributes before and after optimization.

Click on the link to read the full text：https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2021.700365/full