Fluid transmission networks such as water, wastewater, oil, and gas pipelines inherently dissipate hydraulic energy through pressure drops, turbulence, and frictional losses. Harnessing this residual energy requires compact, inline solutions that avoid costly structural modifications. This study presents the design, theoretical evaluation, and simulation-based validation of a smart inline micro-hydro power plant featuring a spherical-blade turbine, adaptive electronic control, and a self-cleaning mechanism. Analytical models and CFD simulations confirm that optimized blade curvature reduces turbulence intensity below 5% while sustaining torque outputs above 12 Nm. Simulation trials across pressures of 1.2–3.8 bar and flow rates of 1.5–4.5 L/s predicted stable power generation in the range of 8–58 W, with efficiency exceeding 90% and pressure losses under 2%. The adaptive PID controller maintained output stability within ±5%, while the self-cleaning mechanism preserved blade surface integrity with a cleanliness index above 95%. IoT-based telemetry ensured reliable remote monitoring and predictive maintenance. The system demonstrates stable hydraulic–electrical conversion with low turbulence, minimal pressure loss, and reliable long-term operation under variable flow regimes. This convergence of fluid dynamics theory, adaptive control, and self-cleaning innovation establishes a new paradigm for sustainable energy recovery in modern pipeline infrastructure. • A novel inline micro-hydro power system is designed for fluid transmission networks. • The spherical turbine geometry enhances rotational efficiency and flow continuity. • Adaptive control and IoT integration enable real-time monitoring and power optimization. • Self-cleaning mechanisms prevent sediment buildup and reduce maintenance needs. • Results confirm stable power output and low noise across varied flow regimes.
Building similarity graph...
Analyzing shared references across papers
Loading...
Asgar Hosseinnezhad
Hadi Sabri
Flow Measurement and Instrumentation
University of Tabriz
Building similarity graph...
Analyzing shared references across papers
Loading...
Hosseinnezhad et al. (Tue,) studied this question.
synapsesocial.com/papers/69a765b9badf0bb9e87da308 — DOI: https://doi.org/10.1016/j.flowmeasinst.2026.103237
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: