Scholarlink

<p>Optimisation of water distribution networks (WDNs) for leakage reduction and energy recovery has been extensively investigated through numerical simulations. However, a significant gap remains in the physical validation of these methods because of the lack of scalable laboratory benchmarks. This study addresses this limitation by developing a systematic scaling methodology to translate the Jowitt and Xu benchmark network into a controllable numerical implementation laboratory-scale model, E-NET. The methodology uses a flow-scale factor of 4.33, derived through recursive pipe-diameter adjustment to preserve hydraulic similarity, specifically similarity in head loss and Reynolds number, using commercially available HDPE pipes. To manage leakage, pressure-reducing valves (PRVs) were implemented using a genetic algorithm (GA) to optimise both valve locations and pressure settings under different load conditions. The optimisation followed a pressure-dependent leakage model with a minimum service pressure constraint. Quantitative results showed close agreement between the Jowitt and Xu and the E-NET model, with RMSE values of 0.38&#xa0;m for head loss. The GA successfully identified broadly consistent optimal PRV locations in both networks, particularly under low-demand conditions. The leakage discrepancies were generally small across the tested valve configurations. This work provides a numerically validated basis for a laboratory benchmark platform and represents a first step towards bridging the gap between theoretical optimisation and physical implementation in WDN pressure management.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

From Real to Laboratory Scale: Simulation and Scaling Methodology for a New Benchmark Water Distribution Network Model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月19日

查看全文
  • Giacomo Ferrarese,
  • Abazar Fathi,
  • Stefano Malavasi

摘要

Optimisation of water distribution networks (WDNs) for leakage reduction and energy recovery has been extensively investigated through numerical simulations. However, a significant gap remains in the physical validation of these methods because of the lack of scalable laboratory benchmarks. This study addresses this limitation by developing a systematic scaling methodology to translate the Jowitt and Xu benchmark network into a controllable numerical implementation laboratory-scale model, E-NET. The methodology uses a flow-scale factor of 4.33, derived through recursive pipe-diameter adjustment to preserve hydraulic similarity, specifically similarity in head loss and Reynolds number, using commercially available HDPE pipes. To manage leakage, pressure-reducing valves (PRVs) were implemented using a genetic algorithm (GA) to optimise both valve locations and pressure settings under different load conditions. The optimisation followed a pressure-dependent leakage model with a minimum service pressure constraint. Quantitative results showed close agreement between the Jowitt and Xu and the E-NET model, with RMSE values of 0.38 m for head loss. The GA successfully identified broadly consistent optimal PRV locations in both networks, particularly under low-demand conditions. The leakage discrepancies were generally small across the tested valve configurations. This work provides a numerically validated basis for a laboratory benchmark platform and represents a first step towards bridging the gap between theoretical optimisation and physical implementation in WDN pressure management.