Improved MOPSO based on classification strategy and internal flow analysis in the optimization design of green building pipeline pumps
Year: 2026
Type: article
Abstract: This study solves the low efficiency of green building pipeline pumps by introducing a multi-objective particle swarm optimization algorithm that integrates classification strategy and internal flow analysis. Enhanced through neural networks and update strategies, the algorithm demonstrates superior search accuracy, effectively converging to true Pareto fronts in test functions. Internal flow analysis revealed consistent velocity distribution patterns: under design and large-flow conditions, the fluid accelerated gradually from the inlet then decelerated sharply at cx/cl = 0.7. Under low-flow conditions, it showed rapid acceleration beyond relative position 0.4 with similar deceleration at 0.7. The approximate model achieved high precision, with maximum prediction errors of 1.96 %, 2.37 %, and 0.42 % across different flow rates. The characteristic of optimized design was the extended straight inlet section, which produced a more uniform velocity distribution while maintaining cross-sectional similarity with the original model at a relative position below 0.5. This methodology provides an effective approach for structural optimization of energy-efficient pipeline pumps in green buildings.
Source: Open Computer Science
Author: Bin Zhang
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,187
OpenAlex ID: W7204592995