Probabilistic modeling and reliability-based design optimization of a ground source heat pump system

Zilong Zhao, Yanwen Xu, Yu Feng Lin, Xinlei Wang, Pingfeng Wang

Research output: Contribution to journalArticlepeer-review

Abstract

The optimization design of a ground source heat pump (GSHP) system can be crucial in improving its performance and economic competitiveness. The effect of probabilistic uncertainties of design variables in a GSHP system was analyzed using reliability-based design optimization (RBDO) method. An analytical borehole heat transfer model was selected as the frame of energy simulation in this work. With the goal to minimize the cumulative costs over a 20-year lifespan of the GSHP system, a non-linear optimization was carried out under three constraint factors imposed on the internal flow in ground heat exchanger: The inlet water temperature, water pressure losses and Reynolds number to ensure turbulent flow. Three design variables including depth of boreholes, ground pipe radius and mass flow rate, and two random variables at the installation site, including the groundwater velocity and ground thermal conductivity were considered in this investigation. Different uncertainty levels were assigned into the probability indexes of all five variables, which were studied under multiple reliability levels of all three constraints. Results showed that uncertainties of variables can strongly affect the system reliability and total cost determination. The compromised increment of system cost to ensure the reliability was discussed, and the optimal combinations of design variables (borehole depth, pipe radius and mass flow rate) were also given under different designing scenarios.

Original languageEnglish (US)
Article number117341
JournalApplied Thermal Engineering
Volume197
DOIs
StatePublished - Oct 2021

Keywords

  • First-order reliability method
  • Ground source heat pump
  • Probabilistic uncertainty
  • Reliability-based design optimization

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Industrial and Manufacturing Engineering

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