Abstract
Geothermal pavements comprise a newfound type of thermal geo-structures, an innovation in pavement construction contributing to ground source heat pump (GSHP) systems, resulting in a better cost-efficiency compared to conventional GSHP systems. Ground heat exchangers are formed by embedding pipe into the pavement structure (e.g., base, sub-base or subgrades). Developing accurate models with appropriate boundary conditions is crucial to obtaining a deep understanding of the performance and function of geothermal pavement systems. This paper introduces an experimentally validated 3D finite element model (FEM) and compares the effect of surface boundary condition choices on the results of the simulation. The model uses two different approaches for boundary conditions: i) using ambient temperature and ii) implementing energy balance equations on the surface, as surface boundary conditions. The model is further utilised to perform a parametric study and the results are employed by a statistical tool (Minitab) to determine the optimum heat exchanger design for a geothermal pavement project in Adelaide, an Australian city subjected to temperate climate conditions and good solar irradiation throughout the year. Results show that the choice of both boundary conditions leads to similar fluid temperature trends, but with differences in values, particularly during the heating season, resulting in a reduced embedded pipe length of up to 30 % and up to a 10 % better annual average coefficient of performance (COP (when using the energy balance equations on the surface. The parametric study shows that the system annual COP increases with the length of the pipe and spacing between the pipes, whilst the pipe depth placement is inversely proportional to the annual average COP given the advantages obtained during the heating season over the cooling season. Finally, considering the interaction effect between various parameters, a pipe heat exchanger with 400 m length, 0.76 m pipe spacing and 0.45 burial depth is evaluated as the best design to maximise COP for the case study at hand.