Impact of simplifications on numerical modelling of the shallow subsurface at city-scale and implications for shallow geothermal potential

Makasis, Nikolas; Kreitmair, Monika Johanna; Bidarmaghz, Asal; Farr, Gareth; Scheidegger, Johanna; Choudhary, Ruchi

doi:10.1016/j.scitotenv.2021.148236

Cited by 12 publications

(9 citation statements)

References 52 publications

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“…Such models can also be employed to balance heat fluxes based on implemented surface boundary conditions. Recent modeling results for Cardiff (UK) suggest surface averaged heat fluxes at a rate of 60 mW m −2 at 20 m bgl [86].…”

Section: Tgp Of Shallow Urban Groundwatermentioning

confidence: 99%

The evolution of the geothermal potential of a subsurface urban heat island

Hemmerle

Ferguson

Blum

et al. 2022

Environ. Res. Lett.

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Meeting the rising energy demands of cities is a global challenge. Exploitation of the additional heat in the subsurface associated with the subsurface urban heat island (SUHI) has been proposed to address the heating demands. For the sustainable use of this heat it is crucial to understand how SUHIs evolve. To date, there have been no comprehensive studies showing how temperature anomalies beneath cities change over time scales of decades. Here, we reveal the long-term increase of temperatures in the groundwater beneath Cologne, Germany from 1973 to 2020. The rise in groundwater temperature trails atmospheric temperature rise in the rural areas and exceeds the rise in atmospheric temperature in the urban center. However, the amount of heat that is currently stored each year in the thin shallow aquifer reaches only 1% of the annual heating demand. The majority of the anthropogenic heat passes by the vertical extent of the aquifer or is discharged by the adjacent river. Overall the geothermal resource of the urban ground remains largely underused and heat extraction as well as combined heating and cooling could substantially raise the geothermal potential to supply the city’s demand.

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Section: Tgp Of Shallow Urban Groundwatermentioning

confidence: 99%

The evolution of the geothermal potential of a subsurface urban heat island

Hemmerle

Ferguson

Blum

et al. 2022

Environ. Res. Lett.

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“…The upwards and downwards out-of-plane conductive heat fluxes ( and , respectively) from neighbouring planes are given by where is the effective thermal conductivity (W/(m K)), and represents the temperature at the plane. Further details on the modelling can be found in the cited literature. The dynamics of the River Taff flowing through the domain are modelled using incompressible turbulent single-phase flow physics and coupled to the heat transfer physics in terms of temperature, pressure and velocity, as explained in detail in (Makasis et al, 2021). The Reynolds-averaged Navier-Stokes (RANS) equations are implemented for conservation of momentum, and the continuity equation for conservation of mass.…”

Section: Tablementioning

confidence: 99%

“…This work utilises a validated semi-3D numerical modelling approach (Bidarmaghz et al, 2020; Makasis et al, 2021), to model the subsurface response to thermal and hydraulic phenomena. A collection of - planes is modelled using the governing equations for conductive and convective heat transfer, and fluid flow through porous media, i.e., where is the relative width of the plane (or the distance between planes) (m), is the effective density (kg/m 3 ), the effective specific heat capacity (J/(kg K)), is time (s), is the fluid (groundwater) density (kg/m 3 ), is the specific heat capacity of the fluid (J/(kg K)), is the Darcy velocity of the fluid (m/s), is the heat flux (W/m 2 ), the permeability (m ) of the material is related to the hydraulic conductivity (m/s) by , is the dynamic viscosity of water (Pa s), is the pressure of water (Pa), is the total head gradient (m) and represents an external heat source (W), such as a ground heat exchangers.…”

Section: Tablementioning

confidence: 99%

“…The dynamics of the River Taff flowing through the domain are modelled using incompressible turbulent single-phase flow physics and coupled to the heat transfer physics in terms of temperature, pressure and velocity, as explained in detail in (Makasis et al, 2021). The Reynolds-averaged Navier-Stokes (RANS) equations are implemented for conservation of momentum, and the continuity equation for conservation of mass.…”

Section: Tablementioning

confidence: 99%

See 1 more Smart Citation

Bayesian parameter inference for shallow subsurface modeling using field data and impacts on geothermal planning

Kreitmair

Makasis²,

Menberg³

et al. 2022

DCE

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Understanding the subsurface is crucial in building a sustainable future, particularly for urban centers. Importantly, the thermal effects that anthropogenic infrastructure, such as buildings, tunnels, and ground heat exchangers, can have on this shared resource need to be well understood to avoid issues, such as overheating the ground, and to identify opportunities, such as extracting and utilizing excess heat. However, obtaining data for the subsurface can be costly, typically requiring the drilling of boreholes. Bayesian statistical methodologies can be used towards overcoming this, by inferring information about the ground by combining field data and numerical modeling, while quantifying associated uncertainties. This work utilizes data obtained in the city of Cardiff, UK, to evaluate the applicability of a Bayesian calibration (using GP surrogates) approach to measured data and associated challenges (previously not tested) and to obtain insights on the subsurface of the area. The importance of the data set size is analyzed, showing that more data are required in realistic (field data), compared to controlled conditions (numerically-generated data), highlighting the importance of identifying data points that contain the most information. Heterogeneity of the ground (i.e., input parameters), which can be particularly prominent in large-scale subsurface domains, is also investigated, showing that the calibration methodology can still yield reasonably accurate results under heterogeneous conditions. Finally, the impact of considering uncertainty in subsurface properties is demonstrated in an existing shallow geothermal system in the area, showing a higher than utilized ground capacity, and the potential for a larger scale system given sufficient demand.

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“…This is the reason why the geoengineering characteristics depend on the scale of the project are often approximated with different techniques and computer modelling approaches [14][15][16][17]. Producing 3D conceptual shallow geothermal potentials [18,19], utilizing GIS [20][21][22], spatial data analysis [23][24][25], applying numerical technique [26][27][28][29], integrated of different geophysical prospecting techniques such as magnetotelluric [30][31][32][33][34], gravity [33,35], seismic [31,33], and electrical resistivity [31,36,37], as well as evident geological characteristics [38,39] are some of the carried efforts in Spain, Chile, Pakistan, Iran, India, Nigeria, Indonesia, Denmark, China, Thailand, Italy, Taiwan, Finland and Japan. However, simulating the geothermal resources using numerical techniques due to complexity of the model preparation (natural state properties of the rocks and geothermal system), description of the realistic problem and evaluation of the results as well as inability in providing any insight into generalizations is a very time-consuming task that demands extensive experience.…”

Section: Introductionmentioning

confidence: 99%

Generating 3D Geothermal Maps in Catalonia, Spain Using a Hybrid Adaptive Multitask Deep Learning Procedure

et al. 2022

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Mapping the subsurface temperatures can efficiently lead to identifying the geothermal distribution heat flow and potential hot spots at different depths. In this paper, an advanced adaptive multitask deep learning procedure for 3D spatial mapping of the subsurface temperature was proposed. As a result, predictive 3D spatial subsurface temperatures at different depths were successfully generated using geolocation of 494 exploratory boreholes data in Catalonia (Spain). To increase the accuracy of the achieved results, hybridization with a new modified firefly algorithm was carried out. Subsequently, uncertainty analysis using a novel automated ensemble deep learning approach for the predicted temperatures and generated spatial 3D maps were executed. Comparing the accuracy performances in terms of correct classification rate (CCR) and the area under the precision–recall curves for validation and whole datasets with at least 4.93% and 2.76% improvement indicated for superiority of the hybridized model. According to the results, the efficiency of the proposed hybrid multitask deep learning in 3D geothermal characterization to enhance the understanding and predictability of subsurface spatial distribution of temperatures is inferred. This implies that the applicability and cost effectiveness of the adaptive procedure in producing 3D high resolution depth dependent temperatures can lead to locate prospective geothermally hotspot active regions.

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Impact of simplifications on numerical modelling of the shallow subsurface at city-scale and implications for shallow geothermal potential

Cited by 12 publications

References 52 publications

The evolution of the geothermal potential of a subsurface urban heat island

The evolution of the geothermal potential of a subsurface urban heat island

Bayesian parameter inference for shallow subsurface modeling using field data and impacts on geothermal planning

Generating 3D Geothermal Maps in Catalonia, Spain Using a Hybrid Adaptive Multitask Deep Learning Procedure

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