# CFD and Experimental Comparison for Micro-Pump Performance in Space Applications: A Case Study
## 1 Introduction
[PERSON] et al. [13] demonstrated that the implementation of a vaned diffuser enhanced pressure recovery and reduced hydraulic losses, with pressure fluctuations decreasing further from the impeller outlet--indicating improved stability. Complementary work by [PERSON] et al. [14] achieved a 4% increase in efficiency and an 8% rise in head through optimized diffuser geometry. Nonetheless, challenges remain. Cavitation-induced instabilities, particularly rotating cavitation, continue to generate severe force oscillations in centrifugal turbopumps [15]. Additionally, surface roughness has been identified as a critical factor affecting both efficiency and head, opening new possibilities for refinement in material selection and manufacturing processes [16]. To address these multifaceted issues, [PERSON] et al. [17] integrated turbomachinery theory with advanced numerical analysis and optimization strategies, achieving notable improvements in both impeller and volute designs that surpassed initial performance expectations. Other studies have employed genetic algorithms and CFD simulations, with significant pressure and head improvements observed. [PERSON] et al. [18] utilized a genetic algorithm (GA) coupled with computational fluid dynamics (CFD) to perform a multi-objectiveoptimization of a centrifugal pump, focusing on the interaction between the impeller and diffuser. The optimized design achieved a head increase of approximately 25.74% under operating conditions, demonstrating significant improvements in performance due to the optimized impeller-diffuser interaction. Design optimization of a centrifugal pump with backward-curved blades using surrogate modeling and three-dimensional steady Reynolds-averaged Navier-Stokes (RANS) analysis. The study demonstrated that the optimized design led to enhanced hydraulic performance [19]. Numerical simulation, surrogate modeling, and genetic algorithms have enhanced the efficiency of residual heat removal pumps (RHRP), achieving an 8.34% increase compared to original simulations due to improved pressure distributions and reduced flow separation [20]. Multi-objective optimization techniques have also aimed to simultaneously increase efficiency and decrease the required net positive suction head (NPSHr) of centrifugal pumps, utilizing genetic algorithms and neural network modeling to identify design trade-offs for optimal performance [21]. Integrating artificial intelligence algorithms for pump design optimization has shown promising results in improving centrifugal pump performance. [PERSON] et al. [22] study employed a multi-objective optimization approach for a centrifugal pump impeller, integrating a Kriging surrogate model with a multi-island genetic algorithm. The optimization process led to significant improvements in pump performance. Specifically, the optimized impeller design achieved a head increase of 6.73%, an efficiency enhancement of 3.85%, and a reduction in the net positive suction head required (NPSHr) by 3.4%. Another study used a modified particle swarm optimization (PSO) algorithm in conjunction with an artificial neural network (ANN) surrogate model. The ANN was trained using data from CFD simulations to predict pump performance based on design parameters, allowing for efficient exploration of the design space. The optimization resulted in a notable improvement in pump efficiency. The study reported an increase in efficiency at the design point by approximately 0.454% [23]. These innovative approaches, which consider multiple parameters and refine objective functions, offer valuable insights into enhancing pump efficiency and reducing operational costs. [PERSON] et al. [24] introduced an optimal hydraulic design method for centrifugal pump impellers, integrating two-dimensional hydraulic design theory, genetic algorithms (GA), and boundary vorticity flux diagnosis.