The dynamic response of pump valve motion directly influences the volumetric efficiency of drilling pumps and serves as a critical factor in performance enhancement. This study presents a coupled fluid–structure interaction (FSI) analysis of a novel secondary cushioned pump valve for drilling systems. A validated 3D transient numerical model, integrating piston–valve kinematic coupling and clearance threshold modeling, was developed to resolve the dynamic interactions between reciprocating mechanisms and turbulent flow fields. The methodology addresses critical limitations in conventional valve closure simulations by incorporating a geometrically adaptive mesh refinement strategy while maintaining computational stability. Transient velocity profiles confirm complete sealing integrity with near-zero leakage (<0.01 m/s), while a 39.3 MPa inter-pipeline pressure differential induces 16% higher jet velocities in suction valves compared to discharge counterparts. The secondary cushioned valve design reduces closure hysteresis by 22%, enhancing volumetric efficiency under rated conditions. Parametric studies reveal structural dominance, with increases in cylindrical spring stiffness lowering discharge valve lift by 7.2% and velocity amplitude by 2.74%, while wave spring optimization (24% stiffness enhancement) eliminates pressure decay and reduces perturbations by 90%. Operational sensitivity analysis demonstrates stroke frequency as a critical failure determinant: elevating speed from 90 to 120 rpm amplifies suction valve peak velocity by 59.87% and initial closing shock by 129.07%. Transient flow simulations validate configuration-dependent performance, showing 6.3 ± 0.1% flow rate deviations from theoretical predictions (Qt_max = 40.0316 kg/s) due to kinematic hysteresis. This study establishes spring parameter modulation as a key strategy for balancing flow stability and mitigating cushioning-induced oscillations. These findings provide actionable insights for optimizing high-pressure pump systems through hysteresis control and parametric adaptation.
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Wu et al. (2025) studied this question.
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