The effect of nozzle construction factors on hydraulic performance and prediction

Shengqun Jiang; Jixiang Yue; Yancong Liu; Yangli Zhou

doi:10.1117/12.3025411

28 February 2024 The effect of nozzle construction factors on hydraulic performance and prediction

Shengqun Jiang, Jixiang Yue, Yancong Liu, Yangli Zhou

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 130710C (2024) https://doi.org/10.1117/12.3025411
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

To investigate the connection between nozzle jet performance and structural characteristics (contraction angle θ, outlet diameter d, ratio of straight segment to outlet diameter L2/d), impact force studies were performed on nine nozzles with varied structures using a self-developed water jet experimental platform, with target distances of 20mm, 100 mm, 200 mm, and 300 mm with jet pressures of 0.1 MPa, 0.2 MPa, and 0.3 MPa. The impact force of a nozzle water jet grows dramatically as the outlet diameter increases. When the pressure of the water jet remains constant, the impact force increases as the target distance increases. The maximum water jet impact force is 6.1 KG when the d is 11 mm. The BP neural network, the PSO and the GA-BP neural networks were utilized to forecast and assess the nozzle impact force at a target distance of 300 mm, respectively. The results reveal that, when compared to the PSO and the BP neural network, the GA-BP neural network projected values are more consistent with the measured values, with a lower average error rate and greater predictive capacity.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shengqun Jiang, Jixiang Yue, Yancong Liu, and Yangli Zhou "The effect of nozzle construction factors on hydraulic performance and prediction", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 130710C (28 February 2024); https://doi.org/10.1117/12.3025411

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KEYWORDS

Nozzles

Neural networks

Particle swarm optimization

Sensors

Abrasives

Design

Engineering

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