Open Access

REVIEW

Recent Advances and Future Directions in Centrifugal Slurry Pump Design Optimization: A Lifecycle-Oriented Review

Jianping Yuan, Chenxin Yu, Yanxia Fu^*, Weidong Wang, Heng Liao

Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang, China

* Corresponding Author: Yanxia Fu. Email:

(This article belongs to the Special Issue: Thermal Convection in Multiphase Fluids and Advanced Materials, Integrated Analysis, Material Selection, and Heat Transfer Optimization)

Fluid Dynamics & Materials Processing 2026, 22(6), 12 https://doi.org/10.32604/fdmp.2026.083416

Received 03 April 2026; Accepted 15 June 2026; Issue published 30 June 2026

Abstract

Slurry transport is a critical multiphase-flow process in mining, metallurgy, and dredging applications, where hydraulic efficiency, particle-induced wear, cavitation erosion, and structural vibration are strongly coupled. This topic-focused review synthesizes recent advances in centrifugal slurry pump design optimization from the perspectives of wear-resistant surface engineering, hydraulic design, structural dynamics, intelligent optimization algorithms, and multiphysics simulation. Unlike earlier reviews that primarily addressed hydraulic performance, erosion wear, flow visualization, or numerical modeling in isolation, the present work adopts a lifecycle-oriented perspective. Representative studies are critically evaluated according to reported efficiency improvements, wear-rate and material-loss reduction, cavitation and net-positive-suction-head-related performance changes, validation strategies, uncertainty sources, and practical engineering feasibility. Particular attention is devoted to the integration of computational fluid dynamics with the discrete element method, fluid-structure interaction, cavitation-erosion coupling, particle-size effects, surrogate-assisted optimization, and digital-twin-enabled monitoring frameworks. The reviewed literature indicates that high-fidelity simulations and intelligent algorithms have significantly enhanced design exploration and predictive capability. However, their large-scale engineering deployment remains limited by challenges associated with model validation, data availability, computational cost, interpretability, and generalization under variable slurry conditions. Finally, digital-twin-enabled lifecycle optimization is discussed as a promising conceptual pathway rather than a fully validated industrial solution, highlighting the need for reduced-order modeling, robust sensing strategies, uncertainty-aware data assimilation, and staged experimental validation to support reliable real-world implementation.

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