Liu, Quan
[VerfasserIn];
Weiland, Finn
[VerfasserIn];
Pärisch, Peter
[VerfasserIn];
Kracht, Niklas
[VerfasserIn];
Huang, Mu
[VerfasserIn];
Ptak, Thomas
[VerfasserIn]
Influence of site characteristics on the performance of shallow borehole heat exchanger arrays: A sensitivity analysis
- [published Version]
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E-Artikel;
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Titel:
Influence of site characteristics on the performance of shallow borehole heat exchanger arrays: A sensitivity analysis
Beteiligte:
Liu, Quan
[VerfasserIn];
Weiland, Finn
[VerfasserIn];
Pärisch, Peter
[VerfasserIn];
Kracht, Niklas
[VerfasserIn];
Huang, Mu
[VerfasserIn];
Ptak, Thomas
[VerfasserIn]
Anmerkungen:
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Beschreibung:
Using borehole heat exchangers (BHE) to extract shallow geothermal energy has grown rapidly in recent years. How to maintain a long-term balanced, sustainable operation of the BHE arrays becomes a key issue. Site characteristics, including hydrogeological and geothermal conditions as well as spatial distribution of subsurface physical parameters, are generally idealized or partly neglected in practice. To investigate the consequences of such simplifications, a 3D model, based on data from a planned real site, is established to consider groundwater flow, subsurface heterogeneity, and various hydrogeothermal boundary conditions. After verifying the model, a sensitivity analysis is conducted. Nine factors regarding site characteristics are selected, including Darcy flux in the aquifer, thermal conductivity and volumetric heat capacity of the ground, groundwater depth, thermal and hydraulic heterogeneity, geothermal gradient, heat transfer coefficient on the ground surface and seasonal rainfall. Sensitivity results indicate that the annual total heat production is most sensitive to geothermal gradient, followed by Darcy flux, both of which have positive effects. Groundwater depth and heat transfer coefficient have negative effects on annual heat production. Compared to thermal heterogeneity and rainfall, the influence of hydraulic heterogeneity is higher. Uncertainty of annual heat production due to various Peclet numbers is quantified through regression analysis.